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arxiv-660001
|
2409.13654
|
Neural filtering for Neural Network-based Models of Dynamic Systems
|
<|reference_start|>Neural filtering for Neural Network-based Models of Dynamic Systems: The application of neural networks in modeling dynamic systems has become prominent due to their ability to estimate complex nonlinear functions. Despite their effectiveness, neural networks face challenges in long-term predictions, where the prediction error diverges over time, thus degrading their accuracy. This paper presents a neural filter to enhance the accuracy of long-term state predictions of neural network-based models of dynamic systems. Motivated by the extended Kalman filter, the neural filter combines the neural network state predictions with the measurements from the physical system to improve the estimated state's accuracy. The neural filter's improvements in prediction accuracy are demonstrated through applications to four nonlinear dynamical systems. Numerical experiments show that the neural filter significantly improves prediction accuracy and bounds the state estimate covariance, outperforming the neural network predictions.<|reference_end|>
|
arxiv
|
@article{oveissi2024neural,
title={Neural filtering for Neural Network-based Models of Dynamic Systems},
author={Parham Oveissi, Turibius Rozario, Ankit Goel},
journal={arXiv preprint arXiv:2409.13654},
year={2024},
archivePrefix={arXiv},
eprint={2409.13654},
primaryClass={cs.LG math.DS}
}
|
oveissi2024neural
|
arxiv-660002
|
2409.13655
|
Adaptive Mixture Importance Sampling for Automated Ads Auction Tuning
|
<|reference_start|>Adaptive Mixture Importance Sampling for Automated Ads Auction Tuning: This paper introduces Adaptive Mixture Importance Sampling (AMIS) as a novel approach for optimizing key performance indicators (KPIs) in large-scale recommender systems, such as online ad auctions. Traditional importance sampling (IS) methods face challenges in dynamic environments, particularly in navigating through complexities of multi-modal landscapes and avoiding entrapment in local optima for the optimization task. Instead of updating importance weights and mixing samples across iterations, as in canonical adaptive IS and multiple IS, our AMIS framework leverages a mixture distribution as the proposal distribution and dynamically adjusts both the mixture parameters and their mixing rates at each iteration, thereby enhancing search diversity and efficiency. Through extensive offline simulations, we demonstrate that AMIS significantly outperforms simple Gaussian Importance Sampling (GIS), particularly in noisy environments. Moreover, our approach is validated in real-world scenarios through online A/B experiments on a major search engine, where AMIS consistently identifies optimal tuning points that are more likely to be adopted as mainstream configurations. These findings indicate that AMIS enhances convergence in noisy environments, leading to more accurate and reliable decision-making in the context of importance sampling off-policy estimators.<|reference_end|>
|
arxiv
|
@article{jia2024adaptive,
title={Adaptive Mixture Importance Sampling for Automated Ads Auction Tuning},
author={Yimeng Jia, Kaushal Paneri, Rong Huang, Kailash Singh Maurya, Pavan
Mallapragada, Yifan Shi},
journal={arXiv preprint arXiv:2409.13655},
year={2024},
archivePrefix={arXiv},
eprint={2409.13655},
primaryClass={cs.LG stat.AP}
}
|
jia2024adaptive
|
arxiv-660003
|
2409.13656
|
Exploring Actions, Interactions and Challenges in Software Modelling Tasks: An Empirical Investigation with Students
|
<|reference_start|>Exploring Actions, Interactions and Challenges in Software Modelling Tasks: An Empirical Investigation with Students: Background: Software modelling is a creative yet challenging task. Modellers often find themselves lost in the process, from understanding the modelling problem to solving it with proper modelling strategies and modelling tools. Students learning modelling often get overwhelmed with the notations and tools. To teach students systematic modelling, we must investigate students' practical modelling knowledge and the challenges they face while modelling. Aim: We aim to explore students' modelling knowledge and modelling actions. Further, we want to investigate students' challenges while solving a modelling task on specific modelling tools. Method: We conducted an empirical study by observing 16 pairs of students from two universities and countries solving modelling tasks for one hour. Results: We find distinct patterns of modelling of class and sequence diagrams based on individual modelling styles, the tools' interface and modelling knowledge. We observed how modelling tools influence students' modelling styles and how they can be used to foster students' confidence and creativity. Based on these observations, we developed a set of guidelines aimed at enhancing modelling education and helping students acquire practical modelling skills. Conclusions: The guidance for modelling in education needs to be structured and systematic. Our findings reveal that different modelling styles exist, which should be properly studied. It is essential to nurture the creative aspect of a modeller, particularly while they are still students. Therefore, selecting the right tool is important, and students should understand how a tool can influence their modelling style.<|reference_end|>
|
arxiv
|
@article{chakraborty2024exploring,
title={Exploring Actions, Interactions and Challenges in Software Modelling
Tasks: An Empirical Investigation with Students},
author={Shalini Chakraborty and Javier Troya and Lola Burgue~no and Grischa
Liebel},
journal={arXiv preprint arXiv:2409.13656},
year={2024},
archivePrefix={arXiv},
eprint={2409.13656},
primaryClass={cs.SE}
}
|
chakraborty2024exploring
|
arxiv-660004
|
2409.13661
|
Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models
|
<|reference_start|>Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models: Simulation-based testing is widely used to assess the reliability of Autonomous Driving Systems (ADS), but its effectiveness is limited by the operational design domain (ODD) conditions available in such simulators. To address this limitation, in this work, we explore the integration of generative artificial intelligence techniques with physics-based simulators to enhance ADS system-level testing. Our study evaluates the effectiveness and computational overhead of three generative strategies based on diffusion models, namely instruction-editing, inpainting, and inpainting with refinement. Specifically, we assess these techniques' capabilities to produce augmented simulator-generated images of driving scenarios representing new ODDs. We employ a novel automated detector for invalid inputs based on semantic segmentation to ensure semantic preservation and realism of the neural generated images. We then perform system-level testing to evaluate the ADS's generalization ability to newly synthesized ODDs. Our findings show that diffusion models help increase the ODD coverage for system-level testing of ADS. Our automated semantic validator achieved a percentage of false positives as low as 3\%, retaining the correctness and quality of the generated images for testing. Our approach successfully identified new ADS system failures before real-world testing.<|reference_end|>
|
arxiv
|
@article{baresi2024efficient,
title={Efficient Domain Augmentation for Autonomous Driving Testing Using
Diffusion Models},
author={Luciano Baresi, Davide Yi Xian Hu, Andrea Stocco, Paolo Tonella},
journal={arXiv preprint arXiv:2409.13661},
year={2024},
archivePrefix={arXiv},
eprint={2409.13661},
primaryClass={cs.SE}
}
|
baresi2024efficient
|
arxiv-660005
|
2409.13664
|
Analysis of Gene Regulatory Networks from Gene Expression Using Graph Neural Networks
|
<|reference_start|>Analysis of Gene Regulatory Networks from Gene Expression Using Graph Neural Networks: Unraveling the complexities of Gene Regulatory Networks (GRNs) is crucial for understanding cellular processes and disease mechanisms. Traditional computational methods often struggle with the dynamic nature of these networks. This study explores the use of Graph Neural Networks (GNNs), a powerful approach for modeling graph-structured data like GRNs. Utilizing a Graph Attention Network v2 (GATv2), our study presents a novel approach to the construction and interrogation of GRNs, informed by gene expression data and Boolean models derived from literature. The model's adeptness in accurately predicting regulatory interactions and pinpointing key regulators is attributed to advanced attention mechanisms, a hallmark of the GNN framework. These insights suggest that GNNs are primed to revolutionize GRN analysis, addressing traditional limitations and offering richer biological insights. The success of GNNs, as highlighted by our model's reliance on high-quality data, calls for enhanced data collection methods to sustain progress. The integration of GNNs in GRN research is set to pioneer developments in personalized medicine, drug discovery, and our grasp of biological systems, bolstered by the structural analysis of networks for improved node and edge prediction.<|reference_end|>
|
arxiv
|
@article{otal2024analysis,
title={Analysis of Gene Regulatory Networks from Gene Expression Using Graph
Neural Networks},
author={Hakan T. Otal, Abdulhamit Subasi, Furkan Kurt, M. Abdullah Canbaz,
Yasin Uzun},
journal={arXiv preprint arXiv:2409.13664},
year={2024},
archivePrefix={arXiv},
eprint={2409.13664},
primaryClass={cs.LG cs.CE cs.SI}
}
|
otal2024analysis
|
arxiv-660006
|
2409.13665
|
DiffFluid: Plain Diffusion Models are Effective Predictors of Flow Dynamics
|
<|reference_start|>DiffFluid: Plain Diffusion Models are Effective Predictors of Flow Dynamics: We showcase the plain diffusion models with Transformers are effective predictors of fluid dynamics under various working conditions, e.g., Darcy flow and high Reynolds number. Unlike traditional fluid dynamical solvers that depend on complex architectures to extract intricate correlations and learn underlying physical states, our approach formulates the prediction of flow dynamics as the image translation problem and accordingly leverage the plain diffusion model to tackle the problem. This reduction in model design complexity does not compromise its ability to capture complex physical states and geometric features of fluid dynamical equations, leading to high-precision solutions. In preliminary tests on various fluid-related benchmarks, our DiffFluid achieves consistent state-of-the-art performance, particularly in solving the Navier-Stokes equations in fluid dynamics, with a relative precision improvement of +44.8%. In addition, we achieved relative improvements of +14.0% and +11.3% in the Darcy flow equation and the airfoil problem with Euler's equation, respectively. Code will be released at https://github.com/DongyuLUO/DiffFluid upon acceptance.<|reference_end|>
|
arxiv
|
@article{luo2024difffluid:,
title={DiffFluid: Plain Diffusion Models are Effective Predictors of Flow
Dynamics},
author={Dongyu Luo, Jianyu Wu, Jing Wang, Hairun Xie, Xiangyu Yue, Shixiang
Tang},
journal={arXiv preprint arXiv:2409.13665},
year={2024},
archivePrefix={arXiv},
eprint={2409.13665},
primaryClass={cs.LG physics.flu-dyn}
}
|
luo2024difffluid:
|
arxiv-660007
|
2409.13668
|
Keypoint Detection Technique for Image-Based Visual Servoing of Manipulators
|
<|reference_start|>Keypoint Detection Technique for Image-Based Visual Servoing of Manipulators: This paper introduces an innovative keypoint detection technique based on Convolutional Neural Networks (CNNs) to enhance the performance of existing Deep Visual Servoing (DVS) models. To validate the convergence of the Image-Based Visual Servoing (IBVS) algorithm, real-world experiments utilizing fiducial markers for feature detection are conducted before designing the CNN-based feature detector. To address the limitations of fiducial markers, the novel feature detector focuses on extracting keypoints that represent the corners of a more realistic object compared to fiducial markers. A dataset is generated from sample data captured by the camera mounted on the robot end-effector while the robot operates randomly in the task space. The samples are automatically labeled, and the dataset size is increased by flipping and rotation. The CNN model is developed by modifying the VGG-19 pre-trained on the ImageNet dataset. While the weights in the base model remain fixed, the fully connected layer's weights are updated to minimize the mean absolute error, defined based on the deviation of predictions from the real pixel coordinates of the corners. The model undergoes two modifications: replacing max-pooling with average-pooling in the base model and implementing an adaptive learning rate that decreases during epochs. These changes lead to a 50 percent reduction in validation loss. Finally, the trained model's reliability is assessed through k-fold cross-validation.<|reference_end|>
|
arxiv
|
@article{amiri2024keypoint,
title={Keypoint Detection Technique for Image-Based Visual Servoing of
Manipulators},
author={Niloufar Amiri, Guanghui Wang, Farrokh Janabi-Sharifi},
journal={arXiv preprint arXiv:2409.13668},
year={2024},
archivePrefix={arXiv},
eprint={2409.13668},
primaryClass={cs.RO}
}
|
amiri2024keypoint
|
arxiv-660008
|
2409.13669
|
A Spacetime Perspective on Dynamical Computation in Neural Information Processing Systems
|
<|reference_start|>A Spacetime Perspective on Dynamical Computation in Neural Information Processing Systems: There is now substantial evidence for traveling waves and other structured spatiotemporal recurrent neural dynamics in cortical structures; but these observations have typically been difficult to reconcile with notions of topographically organized selectivity and feedforward receptive fields. We introduce a new 'spacetime' perspective on neural computation in which structured selectivity and dynamics are not contradictory but instead are complimentary. We show that spatiotemporal dynamics may be a mechanism by which natural neural systems encode approximate visual, temporal, and abstract symmetries of the world as conserved quantities, thereby enabling improved generalization and long-term working memory.<|reference_end|>
|
arxiv
|
@article{keller2024a,
title={A Spacetime Perspective on Dynamical Computation in Neural Information
Processing Systems},
author={T. Anderson Keller, Lyle Muller, Terrence J. Sejnowski, Max Welling},
journal={arXiv preprint arXiv:2409.13669},
year={2024},
archivePrefix={arXiv},
eprint={2409.13669},
primaryClass={q-bio.NC cs.NE}
}
|
keller2024a
|
arxiv-660009
|
2409.13671
|
A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network
|
<|reference_start|>A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network: Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare, as MCC significantly impacts patient outcomes and healthcare costs. Graph neural networks (GNNs) are effective methods for modeling complex graph data, such as those found in MCC. However, a significant challenge with GNNs is their reliance on an existing graph structure, which is not readily available for MCC. To address this challenge, we propose a novel generative framework for GNNs that constructs a representative underlying graph structure by utilizing the distribution of the data to enhance predictive analytics for MCC. Our framework employs a graph variational autoencoder (GVAE) to capture the complex relationships in patient data. This allows for a comprehensive understanding of individual health trajectories and facilitates the creation of diverse patient stochastic similarity graphs while preserving the original feature set. These variations of patient stochastic similarity graphs, generated from the GVAE decoder, are then processed by a GNN using a novel Laplacian regularization technique to refine the graph structure over time and improves the prediction accuracy of MCC. A contextual Bandit is designed to evaluate the stochastically generated graphs and identify the best-performing graph for the GNN model iteratively until model convergence. We validate the performance of the proposed contextual Bandit algorithm against $\varepsilon$-Greedy and multi-armed Bandit algorithms on a large cohort (n = 1,592) of patients with MCC. These advancements highlight the potential of the proposed approach to transform predictive healthcare analytics, enabling a more personalized and proactive approach to MCC management.<|reference_end|>
|
arxiv
|
@article{rico2024a,
title={A Generative Framework for Predictive Modeling of Multiple Chronic
Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph
Neural Network},
author={Julian Carvajal Rico, Adel Alaeddini, Syed Hasib Akhter Faruqui, Susan
P Fisher-Hoch, and Joseph B Mccormick},
journal={arXiv preprint arXiv:2409.13671},
year={2024},
archivePrefix={arXiv},
eprint={2409.13671},
primaryClass={cs.LG}
}
|
rico2024a
|
arxiv-660010
|
2409.13672
|
Recent Advances in Non-convex Smoothness Conditions and Applicability to Deep Linear Neural Networks
|
<|reference_start|>Recent Advances in Non-convex Smoothness Conditions and Applicability to Deep Linear Neural Networks: The presence of non-convexity in smooth optimization problems arising from deep learning have sparked new smoothness conditions in the literature and corresponding convergence analyses. We discuss these smoothness conditions, order them, provide conditions for determining whether they hold, and evaluate their applicability to training a deep linear neural network for binary classification.<|reference_end|>
|
arxiv
|
@article{patel2024recent,
title={Recent Advances in Non-convex Smoothness Conditions and Applicability to
Deep Linear Neural Networks},
author={Vivak Patel, Christian Varner},
journal={arXiv preprint arXiv:2409.13672},
year={2024},
archivePrefix={arXiv},
eprint={2409.13672},
primaryClass={cs.LG math.OC}
}
|
patel2024recent
|
arxiv-660011
|
2409.13675
|
OLiVia-Nav: An Online Lifelong Vision Language Approach for Mobile Robot Social Navigation
|
<|reference_start|>OLiVia-Nav: An Online Lifelong Vision Language Approach for Mobile Robot Social Navigation: Service robots in human-centered environments such as hospitals, office buildings, and long-term care homes need to navigate while adhering to social norms to ensure the safety and comfortability of the people they are sharing the space with. Furthermore, they need to adapt to new social scenarios that can arise during robot navigation. In this paper, we present a novel Online Lifelong Vision Language architecture, OLiVia-Nav, which uniquely integrates vision-language models (VLMs) with an online lifelong learning framework for robot social navigation. We introduce a unique distillation approach, Social Context Contrastive Language Image Pre-training (SC-CLIP), to transfer the social reasoning capabilities of large VLMs to a lightweight VLM, in order for OLiVia-Nav to directly encode social and environment context during robot navigation. These encoded embeddings are used to generate and select robot social compliant trajectories. The lifelong learning capabilities of SC-CLIP enable OLiVia-Nav to update the lightweight VLM with robot trajectory predictions overtime as new social scenarios are encountered. We conducted extensive real-world experiments in diverse social navigation scenarios. The results showed that OLiVia-Nav outperformed existing state-of-the-art DRL and VLM methods in terms of mean squared error, Hausdorff loss, and personal space violation duration. Ablation studies also verified the design choices for OLiVia-Nav.<|reference_end|>
|
arxiv
|
@article{narasimhan2024olivia-nav:,
title={OLiVia-Nav: An Online Lifelong Vision Language Approach for Mobile Robot
Social Navigation},
author={Siddarth Narasimhan, Aaron Hao Tan, Daniel Choi, Goldie Nejat},
journal={arXiv preprint arXiv:2409.13675},
year={2024},
archivePrefix={arXiv},
eprint={2409.13675},
primaryClass={cs.RO}
}
|
narasimhan2024olivia-nav:
|
arxiv-660012
|
2409.13676
|
A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification
|
<|reference_start|>A sound description: Exploring prompt templates and class descriptions to enhance zero-shot audio classification: Audio-text models trained via contrastive learning offer a practical approach to perform audio classification through natural language prompts, such as "this is a sound of" followed by category names. In this work, we explore alternative prompt templates for zero-shot audio classification, demonstrating the existence of higher-performing options. First, we find that the formatting of the prompts significantly affects performance so that simply prompting the models with properly formatted class labels performs competitively with optimized prompt templates and even prompt ensembling. Moreover, we look into complementing class labels by audio-centric descriptions. By leveraging large language models, we generate textual descriptions that prioritize acoustic features of sound events to disambiguate between classes, without extensive prompt engineering. We show that prompting with class descriptions leads to state-of-the-art results in zero-shot audio classification across major ambient sound datasets. Remarkably, this method requires no additional training and remains fully zero-shot.<|reference_end|>
|
arxiv
|
@article{olvera2024a,
title={A sound description: Exploring prompt templates and class descriptions
to enhance zero-shot audio classification},
author={Michel Olvera (S2A, LTCI, IDS), Paraskevas Stamatiadis (S2A, LTCI,
IDS), Slim Essid (IDS, S2A, LTCI)},
journal={arXiv preprint arXiv:2409.13676},
year={2024},
archivePrefix={arXiv},
eprint={2409.13676},
primaryClass={cs.SD cs.AI eess.AS}
}
|
olvera2024a
|
arxiv-660013
|
2409.13678
|
SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience
|
<|reference_start|>SoloParkour: Constrained Reinforcement Learning for Visual Locomotion from Privileged Experience: Parkour poses a significant challenge for legged robots, requiring navigation through complex environments with agility and precision based on limited sensory inputs. In this work, we introduce a novel method for training end-to-end visual policies, from depth pixels to robot control commands, to achieve agile and safe quadruped locomotion. We formulate robot parkour as a constrained reinforcement learning (RL) problem designed to maximize the emergence of agile skills within the robot's physical limits while ensuring safety. We first train a policy without vision using privileged information about the robot's surroundings. We then generate experience from this privileged policy to warm-start a sample efficient off-policy RL algorithm from depth images. This allows the robot to adapt behaviors from this privileged experience to visual locomotion while circumventing the high computational costs of RL directly from pixels. We demonstrate the effectiveness of our method on a real Solo-12 robot, showcasing its capability to perform a variety of parkour skills such as walking, climbing, leaping, and crawling.<|reference_end|>
|
arxiv
|
@article{chane-sane2024soloparkour:,
title={SoloParkour: Constrained Reinforcement Learning for Visual Locomotion
from Privileged Experience},
author={Elliot Chane-Sane, Joseph Amigo, Thomas Flayols, Ludovic Righetti,
Nicolas Mansard},
journal={arXiv preprint arXiv:2409.13678},
year={2024},
archivePrefix={arXiv},
eprint={2409.13678},
primaryClass={cs.RO cs.CV cs.LG}
}
|
chane-sane2024soloparkour:
|
arxiv-660014
|
2409.13682
|
ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot Navigation
|
<|reference_start|>ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory for Robot Navigation: Navigating and understanding complex environments over extended periods of time is a significant challenge for robots. People interacting with the robot may want to ask questions like where something happened, when it occurred, or how long ago it took place, which would require the robot to reason over a long history of their deployment. To address this problem, we introduce a Retrieval-augmented Memory for Embodied Robots, or ReMEmbR, a system designed for long-horizon video question answering for robot navigation. To evaluate ReMEmbR, we introduce the NaVQA dataset where we annotate spatial, temporal, and descriptive questions to long-horizon robot navigation videos. ReMEmbR employs a structured approach involving a memory building and a querying phase, leveraging temporal information, spatial information, and images to efficiently handle continuously growing robot histories. Our experiments demonstrate that ReMEmbR outperforms LLM and VLM baselines, allowing ReMEmbR to achieve effective long-horizon reasoning with low latency. Additionally, we deploy ReMEmbR on a robot and show that our approach can handle diverse queries. The dataset, code, videos, and other material can be found at the following link: https://nvidia-ai-iot.github.io/remembr<|reference_end|>
|
arxiv
|
@article{anwar2024remembr:,
title={ReMEmbR: Building and Reasoning Over Long-Horizon Spatio-Temporal Memory
for Robot Navigation},
author={Abrar Anwar, John Welsh, Joydeep Biswas, Soha Pouya, Yan Chang},
journal={arXiv preprint arXiv:2409.13682},
year={2024},
archivePrefix={arXiv},
eprint={2409.13682},
primaryClass={cs.RO cs.AI cs.CL}
}
|
anwar2024remembr:
|
arxiv-660015
|
2409.13683
|
PrefMMT: Modeling Human Preferences in Preference-based Reinforcement Learning with Multimodal Transformers
|
<|reference_start|>PrefMMT: Modeling Human Preferences in Preference-based Reinforcement Learning with Multimodal Transformers: Preference-based reinforcement learning (PbRL) shows promise in aligning robot behaviors with human preferences, but its success depends heavily on the accurate modeling of human preferences through reward models. Most methods adopt Markovian assumptions for preference modeling (PM), which overlook the temporal dependencies within robot behavior trajectories that impact human evaluations. While recent works have utilized sequence modeling to mitigate this by learning sequential non-Markovian rewards, they ignore the multimodal nature of robot trajectories, which consist of elements from two distinctive modalities: state and action. As a result, they often struggle to capture the complex interplay between these modalities that significantly shapes human preferences. In this paper, we propose a multimodal sequence modeling approach for PM by disentangling state and action modalities. We introduce a multimodal transformer network, named PrefMMT, which hierarchically leverages intra-modal temporal dependencies and inter-modal state-action interactions to capture complex preference patterns. We demonstrate that PrefMMT consistently outperforms state-of-the-art PM baselines on locomotion tasks from the D4RL benchmark and manipulation tasks from the Meta-World benchmark.<|reference_end|>
|
arxiv
|
@article{zhao2024prefmmt:,
title={PrefMMT: Modeling Human Preferences in Preference-based Reinforcement
Learning with Multimodal Transformers},
author={Dezhong Zhao, Ruiqi Wang, Dayoon Suh, Taehyeon Kim, Ziqin Yuan,
Byung-Cheol Min, and Guohua Chen},
journal={arXiv preprint arXiv:2409.13683},
year={2024},
archivePrefix={arXiv},
eprint={2409.13683},
primaryClass={cs.RO}
}
|
zhao2024prefmmt:
|
arxiv-660016
|
2409.13684
|
The FIX Benchmark: Extracting Features Interpretable to eXperts
|
<|reference_start|>The FIX Benchmark: Extracting Features Interpretable to eXperts: Feature-based methods are commonly used to explain model predictions, but these methods often implicitly assume that interpretable features are readily available. However, this is often not the case for high-dimensional data, and it can be hard even for domain experts to mathematically specify which features are important. Can we instead automatically extract collections or groups of features that are aligned with expert knowledge? To address this gap, we present FIX (Features Interpretable to eXperts), a benchmark for measuring how well a collection of features aligns with expert knowledge. In collaboration with domain experts, we propose FIXScore, a unified expert alignment measure applicable to diverse real-world settings across cosmology, psychology, and medicine domains in vision, language and time series data modalities. With FIXScore, we find that popular feature-based explanation methods have poor alignment with expert-specified knowledge, highlighting the need for new methods that can better identify features interpretable to experts.<|reference_end|>
|
arxiv
|
@article{jin2024the,
title={The FIX Benchmark: Extracting Features Interpretable to eXperts},
author={Helen Jin, Shreya Havaldar, Chaehyeon Kim, Anton Xue, Weiqiu You,
Helen Qu, Marco Gatti, Daniel A Hashimoto, Bhuvnesh Jain, Amin Madani, Masao
Sako, Lyle Ungar, Eric Wong},
journal={arXiv preprint arXiv:2409.13684},
year={2024},
archivePrefix={arXiv},
eprint={2409.13684},
primaryClass={cs.LG cs.AI}
}
|
jin2024the
|
arxiv-660017
|
2409.13686
|
The Impact of Large Language Models in Academia: from Writing to Speaking
|
<|reference_start|>The Impact of Large Language Models in Academia: from Writing to Speaking: Large language models (LLMs) are increasingly impacting human society, particularly in textual information. Based on more than 30,000 papers and 1,000 presentations from machine learning conferences, we examined and compared the words used in writing and speaking, representing the first large-scale investigating study of how LLMs influence the two main modes of verbal communication and expression within the same group of people. Our empirical results show that LLM-style words such as "significant" have been used more frequently in abstracts and oral presentations. The impact on speaking is beginning to emerge and is likely to grow in the future, calling attention to the implicit influence and ripple effect of LLMs on human society.<|reference_end|>
|
arxiv
|
@article{geng2024the,
title={The Impact of Large Language Models in Academia: from Writing to
Speaking},
author={Mingmeng Geng, Caixi Chen, Yanru Wu, Dongping Chen, Yao Wan, Pan Zhou},
journal={arXiv preprint arXiv:2409.13686},
year={2024},
archivePrefix={arXiv},
eprint={2409.13686},
primaryClass={cs.CL cs.AI cs.CY cs.DL cs.LG}
}
|
geng2024the
|
arxiv-660018
|
2409.13687
|
A Bottom-Up Approach to Class-Agnostic Image Segmentation
|
<|reference_start|>A Bottom-Up Approach to Class-Agnostic Image Segmentation: Class-agnostic image segmentation is a crucial component in automating image editing workflows, especially in contexts where object selection traditionally involves interactive tools. Existing methods in the literature often adhere to top-down formulations, following the paradigm of class-based approaches, where object detection precedes per-object segmentation. In this work, we present a novel bottom-up formulation for addressing the class-agnostic segmentation problem. We supervise our network directly on the projective sphere of its feature space, employing losses inspired by metric learning literature as well as losses defined in a novel segmentation-space representation. The segmentation results are obtained through a straightforward mean-shift clustering of the estimated features. Our bottom-up formulation exhibits exceptional generalization capability, even when trained on datasets designed for class-based segmentation. We further showcase the effectiveness of our generic approach by addressing the challenging task of cell and nucleus segmentation. We believe that our bottom-up formulation will offer valuable insights into diverse segmentation challenges in the literature.<|reference_end|>
|
arxiv
|
@article{dille2024a,
title={A Bottom-Up Approach to Class-Agnostic Image Segmentation},
author={Sebastian Dille and Ari Blondal and Sylvain Paris and Yau{g}{i}z
Aksoy},
journal={arXiv preprint arXiv:2409.13687},
year={2024},
archivePrefix={arXiv},
eprint={2409.13687},
primaryClass={cs.CV}
}
|
dille2024a
|
arxiv-660019
|
2409.13688
|
Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning
|
<|reference_start|>Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning: Plastic pollution presents an escalating global issue, impacting health and environmental systems, with micro- and nanoplastics found across mediums from potable water to air. Traditional methods for studying these contaminants are labor-intensive and time-consuming, necessitating a shift towards more efficient technologies. In response, this paper introduces micro- and nanoplastics (MiNa), a novel and open-source dataset engineered for the automatic detection and classification of micro and nanoplastics using object detection algorithms. The dataset, comprising scanning electron microscopy images simulated under realistic aquatic conditions, categorizes plastics by polymer type across a broad size spectrum. We demonstrate the application of state-of-the-art detection algorithms on MiNa, assessing their effectiveness and identifying the unique challenges and potential of each method. The dataset not only fills a critical gap in available resources for microplastic research but also provides a robust foundation for future advancements in the field.<|reference_end|>
|
arxiv
|
@article{rezvani2024morphological,
title={Morphological Detection and Classification of Microplastics and
Nanoplastics Emerged from Consumer Products by Deep Learning},
author={Hadi Rezvani, Navid Zarrabi, Ishaan Mehta, Christopher Kolios, Hussein
Ali Jaafar, Cheng-Hao Kao, Sajad Saeedi, Nariman Yousefi},
journal={arXiv preprint arXiv:2409.13688},
year={2024},
archivePrefix={arXiv},
eprint={2409.13688},
primaryClass={cs.CV cs.AI stat.AP stat.ME}
}
|
rezvani2024morphological
|
arxiv-660020
|
2409.13689
|
Temporally Aligned Audio for Video with Autoregression
|
<|reference_start|>Temporally Aligned Audio for Video with Autoregression: We introduce V-AURA, the first autoregressive model to achieve high temporal alignment and relevance in video-to-audio generation. V-AURA uses a high-framerate visual feature extractor and a cross-modal audio-visual feature fusion strategy to capture fine-grained visual motion events and ensure precise temporal alignment. Additionally, we propose VisualSound, a benchmark dataset with high audio-visual relevance. VisualSound is based on VGGSound, a video dataset consisting of in-the-wild samples extracted from YouTube. During the curation, we remove samples where auditory events are not aligned with the visual ones. V-AURA outperforms current state-of-the-art models in temporal alignment and semantic relevance while maintaining comparable audio quality. Code, samples, VisualSound and models are available at https://v-aura.notion.site<|reference_end|>
|
arxiv
|
@article{viertola2024temporally,
title={Temporally Aligned Audio for Video with Autoregression},
author={Ilpo Viertola, Vladimir Iashin, Esa Rahtu},
journal={arXiv preprint arXiv:2409.13689},
year={2024},
archivePrefix={arXiv},
eprint={2409.13689},
primaryClass={cs.CV cs.MM cs.SD eess.AS}
}
|
viertola2024temporally
|
arxiv-660021
|
2409.13690
|
Colorful Diffuse Intrinsic Image Decomposition in the Wild
|
<|reference_start|>Colorful Diffuse Intrinsic Image Decomposition in the Wild: Intrinsic image decomposition aims to separate the surface reflectance and the effects from the illumination given a single photograph. Due to the complexity of the problem, most prior works assume a single-color illumination and a Lambertian world, which limits their use in illumination-aware image editing applications. In this work, we separate an input image into its diffuse albedo, colorful diffuse shading, and specular residual components. We arrive at our result by gradually removing first the single-color illumination and then the Lambertian-world assumptions. We show that by dividing the problem into easier sub-problems, in-the-wild colorful diffuse shading estimation can be achieved despite the limited ground-truth datasets. Our extended intrinsic model enables illumination-aware analysis of photographs and can be used for image editing applications such as specularity removal and per-pixel white balancing.<|reference_end|>
|
arxiv
|
@article{careaga2024colorful,
title={Colorful Diffuse Intrinsic Image Decomposition in the Wild},
author={Chris Careaga and Yau{g}{i}z Aksoy},
journal={arXiv preprint arXiv:2409.13690},
year={2024},
doi={10.1145/3687984},
archivePrefix={arXiv},
eprint={2409.13690},
primaryClass={cs.CV}
}
|
careaga2024colorful
|
arxiv-660022
|
2409.13693
|
Declarative Integration and Management of Large Language Models through Finite Automata: Application to Automation, Communication, and Ethics
|
<|reference_start|>Declarative Integration and Management of Large Language Models through Finite Automata: Application to Automation, Communication, and Ethics: This article introduces an innovative architecture designed to declaratively combine Large Language Models (LLMs) with shared histories, and triggers to identify the most appropriate LLM for a given task. Our approach is general and declarative, relying on the construction of finite automata coupled with an event management system. The developed tool is crafted to facilitate the efficient and complex integration of LLMs with minimal programming effort, especially, but not only, for integrating methods of positive psychology to AI. The flexibility of our technique is demonstrated through applied examples in automation, communication, and ethics.<|reference_end|>
|
arxiv
|
@article{petit2024declarative,
title={Declarative Integration and Management of Large Language Models through
Finite Automata: Application to Automation, Communication, and Ethics},
author={Thierry Petit, Arnault Pachot, Claire Conan-Vrinat and Alexandre
Dubarry},
journal={arXiv preprint arXiv:2409.13693},
year={2024},
archivePrefix={arXiv},
eprint={2409.13693},
primaryClass={cs.FL cs.AI cs.CL cs.ET cs.HC}
}
|
petit2024declarative
|
arxiv-660023
|
2409.13694
|
A Knowledge-Centric Benchmarking Framework and Empirical Study for Retrieval-Augmented Generation
|
<|reference_start|>A Knowledge-Centric Benchmarking Framework and Empirical Study for Retrieval-Augmented Generation: Retrieval-Augmented Generation (RAG) enhances generative models by integrating retrieval mechanisms, which allow these models to access and utilize external knowledge sources. Despite its advantages, RAG encounters significant challenges, particularly in effectively handling real-world queries and mitigating hallucinations. The KDD Cup 2024 CRAG competition brings these issues to the forefront by incorporating both web pages and a mock API as knowledge sources, adding the complexity of parsing HTML before large language models (LLMs) can process the information. In this paper, we propose a novel RAG benchmark designed to address these challenges. Our work provides a comprehensive set of experimental results, offering valuable insights for the study of RAG. We thoroughly examine the entire RAG process, including knowledge source selection, retrieval, organization, and reasoning. Key findings from our study include the impact of automated knowledge source selection using agents and the influence of noise chunks on RAG reasoning. Additionally, we conduct detailed experiments to analyze the effects of various hyperparameters on RAG performance. To support further research, we have made our results, the associated code, and a parsed version of the CRAG dataset publicly available\footnote{https://github.com/USTCAGI/RAG-X}, contributing to the advancement of RAG methodologies and establishing a solid foundation for future work in this domain.<|reference_end|>
|
arxiv
|
@article{yu2024a,
title={A Knowledge-Centric Benchmarking Framework and Empirical Study for
Retrieval-Augmented Generation},
author={Shuo Yu (1 and 2), Mingyue Cheng (1 and 2), Jiqian Yang (1 and 2), Jie
Ouyang (1 and 2) ((1) Anhui Province Key Laboratory of Big Data Analysis and
Application, University of Science and Technology of China (2) State Key
Laboratory of Cognitive Intelligence)},
journal={arXiv preprint arXiv:2409.13694},
year={2024},
archivePrefix={arXiv},
eprint={2409.13694},
primaryClass={cs.CL cs.AI cs.IR}
}
|
yu2024a
|
arxiv-660024
|
2409.13695
|
You Only Use Reactive Attention Slice For Long Context Retrieval
|
<|reference_start|>You Only Use Reactive Attention Slice For Long Context Retrieval: Supporting longer context for Large Language Models (LLM) is a promising direction to advance LLMs. As training a model for a longer context window is computationally expensive, many alternative solutions, such as Retrieval Augmented Generation (RAG), have been used. However, most existing RAG methods adopt embedding-based retrieval that falls short on long contexts. To address such challenges, we propose an attention-based retrieval technique, You Only Use Reactive Attention slice (YOURA). YOURA leverages a novel retrieval heuristic called reaction score to rank the relevance of each sentence in the input context with the query sentence. Intuitively, we measure how the per-token attention score "reacts" to the query and greedily retrieves the most reactive sentences. Internally, YOURA generates a token-indexed vector (called reaction vector) for the whole input context. To map each sentence to the token-indexed vector, we propose an Embedding-Agnostic Sentence Yield (EASY), a best-effort token wiggling algorithm. We evaluate our retrieval technique on three open-source pre-trained LLM models across six LongBench QA datasets. Our technique achieves up to 30% vLLM inference throughput improvement for serving long-context queries with a nearly identical quality score to the simple yet effective truncate-middle approach.<|reference_end|>
|
arxiv
|
@article{soh2024you,
title={You Only Use Reactive Attention Slice For Long Context Retrieval},
author={Yun Joon Soh, Hanxian Huang, Yuandong Tian, Jishen Zhao},
journal={arXiv preprint arXiv:2409.13695},
year={2024},
archivePrefix={arXiv},
eprint={2409.13695},
primaryClass={cs.CL cs.AI cs.IR}
}
|
soh2024you
|
arxiv-660025
|
2409.13697
|
Prompt Baking
|
<|reference_start|>Prompt Baking: Two primary ways to change LLM behavior are prompting and weight updates (e.g., fine-tuning). Prompting LLMs is simple and effective, specifying the desired changes explicitly in natural language, whereas weight updates provide more expressive and permanent behavior changes, specified implicitly via training on large datasets. We present a technique for "baking" prompts into the weights of an LLM. Prompt Baking converts a prompt $u$ and initial weights $\theta$ to a new set of weights $\theta_u$ such that new "baked" LLM behaves like the original prompted LLM. Mathematically, we minimize the KL divergence between $P_\theta(\cdot | u)$ and $P_{\theta_u}(\cdot)$, where $P$ is the LLM's probability distribution over token sequences. Across all our experiments, we find prompts can be readily baked into weight updates. Baking chain-of-thought prompts improves zero-shot performance on GSM8K, ASDiv, MBPP, ARC-Easy, ARC-Challenge, and CommonsenseQA benchmarks. Baking news headlines directly updates an LLM's knowledge. And baking instructions & personas alleviates "prompt forgetting" over long sequences. Furthermore, stopping baking early creates "half-baked" models, continuously scaling prompt strength. Baked models retain their sensitivity to further prompting and baking, including re-prompting with the baked-in prompt. Surprisingly, the re-prompted models yield further performance gains in instruction following, as well as math reasoning and coding benchmarks. Taking re-prompting and re-baking to the limit yields a form of iterative self-improvement we call Prompt Pursuit, and preliminary results on instruction following exhibit dramatic performance gains. Finally, we discuss implications for AI safety, continuous model updating, enhancing real-time learning capabilities in LLM-based agents, and generating more stable AI personas.<|reference_end|>
|
arxiv
|
@article{bhargava2024prompt,
title={Prompt Baking},
author={Aman Bhargava, Cameron Witkowski, Alexander Detkov, Matt Thomson},
journal={arXiv preprint arXiv:2409.13697},
year={2024},
archivePrefix={arXiv},
eprint={2409.13697},
primaryClass={cs.CL cs.AI}
}
|
bhargava2024prompt
|
arxiv-660026
|
2409.13698
|
Lightweight Transducer Based on Frame-Level Criterion
|
<|reference_start|>Lightweight Transducer Based on Frame-Level Criterion: The transducer model trained based on sequence-level criterion requires a lot of memory due to the generation of the large probability matrix. We proposed a lightweight transducer model based on frame-level criterion, which uses the results of the CTC forced alignment algorithm to determine the label for each frame. Then the encoder output can be combined with the decoder output at the corresponding time, rather than adding each element output by the encoder to each element output by the decoder as in the transducer. This significantly reduces memory and computation requirements. To address the problem of imbalanced classification caused by excessive blanks in the label, we decouple the blank and non-blank probabilities and truncate the gradient of the blank classifier to the main network. This enables the lightweight transducer achieving similar results to transducer. Additionally, we use richer information to predict the probability of blank, achieving superior results to transducer.<|reference_end|>
|
arxiv
|
@article{wan2024lightweight,
title={Lightweight Transducer Based on Frame-Level Criterion},
author={Genshun Wan, Mengzhi Wang, Tingzhi Mao, Hang Chen, Zhongfu Ye},
journal={Proc. Interspeech 2024, 247-251 (2024)},
year={2024},
doi={10.21437/Interspeech.2024-768},
archivePrefix={arXiv},
eprint={2409.13698},
primaryClass={cs.CL cs.SD eess.AS}
}
|
wan2024lightweight
|
arxiv-660027
|
2409.13699
|
Vietnamese Legal Information Retrieval in Question-Answering System
|
<|reference_start|>Vietnamese Legal Information Retrieval in Question-Answering System: In the modern era of rapidly increasing data volumes, accurately retrieving and recommending relevant documents has become crucial in enhancing the reliability of Question Answering (QA) systems. Recently, Retrieval Augmented Generation (RAG) has gained significant recognition for enhancing the capabilities of large language models (LLMs) by mitigating hallucination issues in QA systems, which is particularly beneficial in the legal domain. Various methods, such as semantic search using dense vector embeddings or a combination of multiple techniques to improve results before feeding them to LLMs, have been proposed. However, these methods often fall short when applied to the Vietnamese language due to several challenges, namely inefficient Vietnamese data processing leading to excessive token length or overly simplistic ensemble techniques that lead to instability and limited improvement. Moreover, a critical issue often overlooked is the ordering of final relevant documents which are used as reference to ensure the accuracy of the answers provided by LLMs. In this report, we introduce our three main modifications taken to address these challenges. First, we explore various practical approaches to data processing to overcome the limitations of the embedding model. Additionally, we enhance Reciprocal Rank Fusion by normalizing order to combine results from keyword and vector searches effectively. We also meticulously re-rank the source pieces of information used by LLMs with Active Retrieval to improve user experience when refining the information generated. In our opinion, this technique can also be considered as a new re-ranking method that might be used in place of the traditional cross encoder. Finally, we integrate these techniques into a comprehensive QA system, significantly improving its performance and reliability<|reference_end|>
|
arxiv
|
@article{ba2024vietnamese,
title={Vietnamese Legal Information Retrieval in Question-Answering System},
author={Thiem Nguyen Ba, Vinh Doan The, Tung Pham Quang, Toan Tran Van},
journal={arXiv preprint arXiv:2409.13699},
year={2024},
archivePrefix={arXiv},
eprint={2409.13699},
primaryClass={cs.IR}
}
|
ba2024vietnamese
|
arxiv-660028
|
2409.13700
|
MAS4POI: a Multi-Agents Collaboration System for Next POI Recommendation
|
<|reference_start|>MAS4POI: a Multi-Agents Collaboration System for Next POI Recommendation: LLM-based Multi-Agent Systems have potential benefits of complex decision-making tasks management across various domains but their applications in the next Point-of-Interest (POI) recommendation remain underexplored. This paper proposes a novel MAS4POI system designed to enhance next POI recommendations through multi-agent interactions. MAS4POI supports Large Language Models (LLMs) specializing in distinct agents such as DataAgent, Manager, Analyst, and Navigator with each contributes to a collaborative process of generating the next POI recommendations.The system is examined by integrating six distinct LLMs and evaluated by two real-world datasets for recommendation accuracy improvement in real-world scenarios. Our code is available at https://github.com/yuqian2003/MAS4POI.<|reference_end|>
|
arxiv
|
@article{wu2024mas4poi:,
title={MAS4POI: a Multi-Agents Collaboration System for Next POI Recommendation},
author={Yuqian Wu, Yuhong Peng, Jiapeng Yu, and Raymond S. T. Lee},
journal={arXiv preprint arXiv:2409.13700},
year={2024},
archivePrefix={arXiv},
eprint={2409.13700},
primaryClass={cs.IR cs.AI cs.SI}
}
|
wu2024mas4poi:
|
arxiv-660029
|
2409.13701
|
CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction
|
<|reference_start|>CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat Interaction: Effective communication in automated chat systems hinges on the ability to understand and respond to context. Traditional models often struggle with determining when additional context is necessary for generating appropriate responses. This paper introduces Context-Aware BERT (CA-BERT), a transformer-based model specifically fine-tuned to address this challenge. CA-BERT innovatively applies deep learning techniques to discern context necessity in multi-turn chat interactions, enhancing both the relevance and accuracy of responses. We describe the development of CA-BERT, which adapts the robust architecture of BERT with a novel training regimen focused on a specialized dataset of chat dialogues. The model is evaluated on its ability to classify context necessity, demonstrating superior performance over baseline BERT models in terms of accuracy and efficiency. Furthermore, CA-BERT's implementation showcases significant reductions in training time and resource usage, making it feasible for real-time applications. The results indicate that CA-BERT can effectively enhance the functionality of chatbots by providing a nuanced understanding of context, thereby improving user experience and interaction quality in automated systems. This study not only advances the field of NLP in chat applications but also provides a framework for future research into context-sensitive AI developments.<|reference_end|>
|
arxiv
|
@article{liu2024ca-bert:,
title={CA-BERT: Leveraging Context Awareness for Enhanced Multi-Turn Chat
Interaction},
author={Minghao Liu, Mingxiu Sui, Yi Nan, Cangqing Wang and Zhijie Zhou},
journal={arXiv preprint arXiv:2409.13701},
year={2024},
archivePrefix={arXiv},
eprint={2409.13701},
primaryClass={cs.CL cs.AI}
}
|
liu2024ca-bert:
|
arxiv-660030
|
2409.13702
|
Shaping the Future of Endangered and Low-Resource Languages -- Our Role in the Age of LLMs: A Keynote at ECIR 2024
|
<|reference_start|>Shaping the Future of Endangered and Low-Resource Languages -- Our Role in the Age of LLMs: A Keynote at ECIR 2024: Isidore of Seville is credited with the adage that it is language that gives birth to a people, and not the other way around , underlining the profound role played by language in the formation of cultural and social identity. Today, of the more than 7100 languages listed, a significant number are endangered. Since the 1970s, linguists, information seekers and enthusiasts have helped develop digital resources and automatic tools to support a wide range of languages, including endangered ones. The advent of Large Language Model (LLM) technologies holds both promise and peril. They offer unprecedented possibilities for the translation and generation of content and resources, key elements in the preservation and revitalisation of languages. They also present threat of homogenisation, cultural oversimplification and the further marginalisation of already vulnerable languages. The talk this paper is based on has proposed an initiatory journey, exploring the potential paths and partnerships between technology and tradition, with a particular focus on the Occitan language. Occitan is a language from Southern France, parts of Spain and Italy that played a major cultural and economic role, particularly in the Middle Ages. It is now endangered according to UNESCO. The talk critically has examined how human expertise and artificial intelligence can work together to offer hope for preserving the linguistic diversity that forms the foundation of our global and especially our European heritage while addressing some of the ethical and practical challenges that accompany the use of these powerful technologies. This paper is based on the keynote I gave at the 46th European Conference on Information Retrieval (ECIR 2024). As an alternative to reading this paper, a video talk is available online. 1 Date: 26 March 2024.<|reference_end|>
|
arxiv
|
@article{mothe2024shaping,
title={Shaping the Future of Endangered and Low-Resource Languages -- Our Role
in the Age of LLMs: A Keynote at ECIR 2024},
author={Josiane Mothe (IRIT-SIG)},
journal={Sigir Forum, 2024, 58 (1), pp.1-13},
year={2024},
doi={10.1145/3687273.3687280},
archivePrefix={arXiv},
eprint={2409.13702},
primaryClass={cs.CL cs.IR}
}
|
mothe2024shaping
|
arxiv-660031
|
2409.13703
|
Zeroshot Listwise Learning to Rank Algorithm for Recommendation
|
<|reference_start|>Zeroshot Listwise Learning to Rank Algorithm for Recommendation: Learning to rank is a rare technology compared with other techniques such as deep neural networks. The number of experts in the field is roughly 1/6 of the number of professionals in deep learning. Being an effective ranking methodology, learning to rank has been widely used in the field of information retrieval. However, in recent years, learning to rank as a recommendation approach has been on decline. In this paper, we take full advantage of order statistic approximation and power law distribution to design a zeroshot listwise learning to rank algorithm for recommendation. We prove in the experiment section that our approach is both accurate and fair.<|reference_end|>
|
arxiv
|
@article{wang2024zeroshot,
title={Zeroshot Listwise Learning to Rank Algorithm for Recommendation},
author={Hao Wang},
journal={arXiv preprint arXiv:2409.13703},
year={2024},
doi={10.1145/3669754.3669821},
archivePrefix={arXiv},
eprint={2409.13703},
primaryClass={cs.IR}
}
|
wang2024zeroshot
|
arxiv-660032
|
2409.13704
|
Entity Extraction from High-Level Corruption Schemes via Large Language Models
|
<|reference_start|>Entity Extraction from High-Level Corruption Schemes via Large Language Models: The rise of financial crime that has been observed in recent years has created an increasing concern around the topic and many people, organizations and governments are more and more frequently trying to combat it. Despite the increase of interest in this area, there is a lack of specialized datasets that can be used to train and evaluate works that try to tackle those problems. This article proposes a new micro-benchmark dataset for algorithms and models that identify individuals and organizations, and their multiple writings, in news articles, and presents an approach that assists in its creation. Experimental efforts are also reported, using this dataset, to identify individuals and organizations in financial-crime-related articles using various low-billion parameter Large Language Models (LLMs). For these experiments, standard metrics (Accuracy, Precision, Recall, F1 Score) are reported and various prompt variants comprising the best practices of prompt engineering are tested. In addition, to address the problem of ambiguous entity mentions, a simple, yet effective LLM-based disambiguation method is proposed, ensuring that the evaluation aligns with reality. Finally, the proposed approach is compared against a widely used state-of-the-art open-source baseline, showing the superiority of the proposed method.<|reference_end|>
|
arxiv
|
@article{koletsis2024entity,
title={Entity Extraction from High-Level Corruption Schemes via Large Language
Models},
author={Panagiotis Koletsis, Panagiotis-Konstantinos Gemos, Christos Chronis,
Iraklis Varlamis, Vasilis Efthymiou, Georgios Th. Papadopoulos},
journal={arXiv preprint arXiv:2409.13704},
year={2024},
archivePrefix={arXiv},
eprint={2409.13704},
primaryClass={cs.CL cs.IR}
}
|
koletsis2024entity
|
arxiv-660033
|
2409.13705
|
Debiasing Text Safety Classifiers through a Fairness-Aware Ensemble
|
<|reference_start|>Debiasing Text Safety Classifiers through a Fairness-Aware Ensemble: Increasing use of large language models (LLMs) demand performant guardrails to ensure the safety of inputs and outputs of LLMs. When these safeguards are trained on imbalanced data, they can learn the societal biases. We present a light-weight, post-processing method for mitigating counterfactual fairness in closed-source text safety classifiers. Our approach involves building an ensemble that not only outperforms the input classifiers and policy-aligns them, but also acts as a debiasing regularizer. We introduce two threshold-agnostic metrics to assess the counterfactual fairness of a model, and demonstrate how combining these metrics with Fair Data Reweighting (FDW) helps mitigate biases. We create an expanded Open AI dataset, and a new templated LLM-generated dataset based on user-prompts, both of which are counterfactually balanced across identity groups and cover four key areas of safety; we will work towards publicly releasing these datasets. Our results show that our approach improves counterfactual fairness with minimal impact on model performance.<|reference_end|>
|
arxiv
|
@article{sturman2024debiasing,
title={Debiasing Text Safety Classifiers through a Fairness-Aware Ensemble},
author={Olivia Sturman, Aparna Joshi, Bhaktipriya Radharapu, Piyush Kumar,
Renee Shelby},
journal={arXiv preprint arXiv:2409.13705},
year={2024},
archivePrefix={arXiv},
eprint={2409.13705},
primaryClass={cs.CL cs.AI cs.LG}
}
|
sturman2024debiasing
|
arxiv-660034
|
2409.13706
|
Decolonising Data Systems: Using Jyutping or Pinyin as tonal representations of Chinese names for data linkage
|
<|reference_start|>Decolonising Data Systems: Using Jyutping or Pinyin as tonal representations of Chinese names for data linkage: Data linkage is increasingly used in health research and policy making and is relied on for understanding health inequalities. However, linked data is only as useful as the underlying data quality, and differential linkage rates may induce selection bias in the linked data. A mechanism that selectively compromises data quality is name romanisation. Converting text of a different writing system into Latin based writing, or romanisation, has long been the standard process of representing names in character-based writing systems such as Chinese, Vietnamese, and other languages such as Swahili. Unstandardised romanisation of Chinese characters, due in part to problems of preserving the correct name orders the lack of proper phonetic representation of a tonal language, has resulted in poor linkage rates for Chinese immigrants. This opinion piece aims to suggests that the use of standardised romanisation systems for Cantonese (Jyutping) or Mandarin (Pinyin) Chinese, which incorporate tonal information, could improve linkage rates and accuracy for individuals with Chinese names. We used 771 Chinese and English names scraped from openly available sources, and compared the utility of Jyutping, Pinyin and the Hong Kong Government Romanisation system (HKG-romanisation) for representing Chinese names. We demonstrate that both Jyutping and Pinyin result in fewer errors compared with the HKG-romanisation system. We suggest that collecting and preserving people's names in their original writing systems is ethically and socially pertinent. This may inform development of language-specific pre-processing and linkage paradigms that result in more inclusive research data which better represents the targeted populations.<|reference_end|>
|
arxiv
|
@article{lam2024decolonising,
title={Decolonising Data Systems: Using Jyutping or Pinyin as tonal
representations of Chinese names for data linkage},
author={Joseph Lam (1), Mario Cortina-Borja (1), Robert Aldridge (2), Ruth
Blackburn (1), Katie Harron (1) ((1) Great Ormond Street Institute of Child
Health, University College London, UK (2) Institute for Health Metrics and
Evaluation, University of Washington, USA)},
journal={arXiv preprint arXiv:2409.13706},
year={2024},
archivePrefix={arXiv},
eprint={2409.13706},
primaryClass={cs.CL}
}
|
lam2024decolonising
|
arxiv-660035
|
2409.13707
|
Retrieval Augmented Generation-Based Incident Resolution Recommendation System for IT Support
|
<|reference_start|>Retrieval Augmented Generation-Based Incident Resolution Recommendation System for IT Support: Clients wishing to implement generative AI in the domain of IT Support and AIOps face two critical issues: domain coverage and model size constraints due to model choice limitations. Clients might choose to not use larger proprietary models such as GPT-4 due to cost and privacy concerns and so are limited to smaller models with potentially less domain coverage that do not generalize to the client's domain. Retrieval augmented generation is a common solution that addresses both of these issues: a retrieval system first retrieves the necessary domain knowledge which a smaller generative model leverages as context for generation. We present a system developed for a client in the IT Support domain for support case solution recommendation that combines retrieval augmented generation (RAG) for answer generation with an encoder-only model for classification and a generative large language model for query generation. We cover architecture details, data collection and annotation, development journey and preliminary validations, expected final deployment process and evaluation plans, and finally lessons learned.<|reference_end|>
|
arxiv
|
@article{isaza2024retrieval,
title={Retrieval Augmented Generation-Based Incident Resolution Recommendation
System for IT Support},
author={Paulina Toro Isaza, Michael Nidd, Noah Zheutlin, Jae-wook Ahn,
Chidansh Amitkumar Bhatt, Yu Deng, Ruchi Mahindru, Martin Franz, Hans
Florian, Salim Roukos},
journal={arXiv preprint arXiv:2409.13707},
year={2024},
archivePrefix={arXiv},
eprint={2409.13707},
primaryClass={cs.IR cs.AI cs.CL}
}
|
isaza2024retrieval
|
arxiv-660036
|
2409.13708
|
Towards Safe Multilingual Frontier AI
|
<|reference_start|>Towards Safe Multilingual Frontier AI: Linguistically inclusive LLMs -- which maintain good performance regardless of the language with which they are prompted -- are necessary for the diffusion of AI benefits around the world. Multilingual jailbreaks that rely on language translation to evade safety measures undermine the safe and inclusive deployment of AI systems. We provide policy recommendations to enhance the multilingual capabilities of AI while mitigating the risks of multilingual jailbreaks. We quantitatively assess the relationship between language resourcedness and model vulnerabilities to multilingual jailbreaks for five frontier large language models across 24 official EU languages. Building on prior research, we propose policy actions that align with the EU legal landscape and institutional framework to address multilingual jailbreaks, while promoting linguistic inclusivity. These include mandatory assessments of multilingual capabilities and vulnerabilities, public opinion research, and state support for multilingual AI development. The measures aim to improve AI safety and functionality through EU policy initiatives, guiding the implementation of the EU AI Act and informing regulatory efforts of the European AI Office.<|reference_end|>
|
arxiv
|
@article{kanepajs2024towards,
title={Towards Safe Multilingual Frontier AI},
author={Art=urs Kanepajs, Vladimir Ivanov, Richard Moulange},
journal={arXiv preprint arXiv:2409.13708},
year={2024},
archivePrefix={arXiv},
eprint={2409.13708},
primaryClass={cs.CL cs.AI cs.CY}
}
|
kanepajs2024towards
|
arxiv-660037
|
2409.13709
|
Column Vocabulary Association (CVA): semantic interpretation of dataless tables
|
<|reference_start|>Column Vocabulary Association (CVA): semantic interpretation of dataless tables: Traditional Semantic Table Interpretation (STI) methods rely primarily on the underlying table data to create semantic annotations. This year's SemTab challenge introduced the ``Metadata to KG'' track, which focuses on performing STI by using only metadata information, without access to the underlying data. In response to this new challenge, we introduce a new term: Column Vocabulary Association (CVA). This term refers to the task of semantic annotation of column headers solely based on metadata information. In this study, we evaluate the performance of various methods in executing the CVA task, including a Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) approach, as well as a more traditional similarity approach with SemanticBERT. Our methodology uses a zero-shot setting, with no pretraining or examples passed to the Large Language Models (LLMs), as we aim to avoid a domain-specific setting. We investigate a total of 7 different LLMs, of which three commercial GPT models (i.e. gpt-3.5-turbo-0.125, gpt-4o and gpt-4-turbo) and four open source models (i.e. llama3-80b, llama3-7b, gemma-7b and mixtral-8x7b). We integrate this models with RAG systems, and we explore how variations in temperature settings affect performances. Moreover, we continue our investigation by performing the CVA task utilizing SemanticBERT, analyzing how various metadata information influence its performance. Initial findings indicate that LLMs generally perform well at temperatures below 1.0, achieving an accuracy of 100\% in certain cases. Nevertheless, our investigation also reveal that the nature of the data significantly influences CVA task outcomes. In fact, in cases where the input data and glossary are related (for example by being created by the same organizations) traditional methods appear to surpass the performance of LLMs.<|reference_end|>
|
arxiv
|
@article{martorana2024column,
title={Column Vocabulary Association (CVA): semantic interpretation of dataless
tables},
author={Margherita Martorana, Xueli Pan, Benno Kruit, Tobias Kuhn, Jacco van
Ossenbruggen},
journal={arXiv preprint arXiv:2409.13709},
year={2024},
archivePrefix={arXiv},
eprint={2409.13709},
primaryClass={cs.CL cs.AI}
}
|
martorana2024column
|
arxiv-660038
|
2409.13710
|
You can remove GPT2's LayerNorm by fine-tuning
|
<|reference_start|>You can remove GPT2's LayerNorm by fine-tuning: The LayerNorm (LN) layer in GPT-style transformer models has long been a hindrance to mechanistic interpretability. LN is a crucial component required to stabilize the training of large language models, and LN or the similar RMSNorm have been used in practically all large language models based on the transformer architecture. The non-linear nature of the LN layers is a hindrance for mechanistic interpretability as it hinders interpretation of the residual stream, and makes it difficult to decompose the model into circuits. Some research have gone so far as to name "reasons interpretability researchers hate layer norm". In this paper we show that it is possible to remove the LN layers from a pre-trained GPT2-small model by fine-tuning on a fraction (500M tokens) of the training data. We demonstrate that this LN-free model achieves similar performance to the original model on the OpenWebText and ThePile datasets (-0.05 cross-entropy loss), and the Hellaswag benchmark (-0.5% accuracy). We provide the fine-tuning procedure and a Hugging Face repository with the fine-tuned GPT2-small models. Our work not only provides a simplified model for mechanistic interpretability research, but also provides evidence that the LN layers, at inference time, do not play a crucial role in transformer models.<|reference_end|>
|
arxiv
|
@article{heimersheim2024you,
title={You can remove GPT2's LayerNorm by fine-tuning},
author={Stefan Heimersheim},
journal={arXiv preprint arXiv:2409.13710},
year={2024},
archivePrefix={arXiv},
eprint={2409.13710},
primaryClass={cs.CL cs.LG}
}
|
heimersheim2024you
|
arxiv-660039
|
2409.13711
|
WebQuest: A Benchmark for Multimodal QA on Web Page Sequences
|
<|reference_start|>WebQuest: A Benchmark for Multimodal QA on Web Page Sequences: The rise of powerful multimodal LLMs has enhanced the viability of building web agents which can, with increasing levels of autonomy, assist users to retrieve information and complete tasks on various human-computer interfaces. It is hence necessary to build challenging benchmarks that span a wide-variety of use cases reflecting real-world usage. In this work, we present WebQuest, a multi-page question-answering dataset that requires reasoning across multiple related web pages. In contrast to existing UI benchmarks that focus on multi-step web navigation and task completion, our dataset evaluates information extraction, multimodal retrieval and composition of information from many web pages. WebQuest includes three question categories: single-screen QA, multi-screen QA, and QA based on navigation traces. We evaluate leading proprietary multimodal models like GPT-4V, Gemini Flash, Claude 3, and open source models like InstructBLIP, PaliGemma on our dataset, revealing a significant gap between single-screen and multi-screen reasoning. Finally, we investigate inference time techniques like Chain-of-Thought prompting to improve model capabilities on multi-screen reasoning.<|reference_end|>
|
arxiv
|
@article{wang2024webquest:,
title={WebQuest: A Benchmark for Multimodal QA on Web Page Sequences},
author={Maria Wang, Srinivas Sunkara, Gilles Baechler, Jason Lin, Yun Zhu,
Fedir Zubach, Lei Shu, Jindong Chen},
journal={arXiv preprint arXiv:2409.13711},
year={2024},
archivePrefix={arXiv},
eprint={2409.13711},
primaryClass={cs.IR cs.AI}
}
|
wang2024webquest:
|
arxiv-660040
|
2409.13712
|
Good Idea or Not, Representation of LLM Could Tell
|
<|reference_start|>Good Idea or Not, Representation of LLM Could Tell: In the ever-expanding landscape of academic research, the proliferation of ideas presents a significant challenge for researchers: discerning valuable ideas from the less impactful ones. The ability to efficiently evaluate the potential of these ideas is crucial for the advancement of science and paper review. In this work, we focus on idea assessment, which aims to leverage the knowledge of large language models to assess the merit of scientific ideas. First, we investigate existing text evaluation research and define the problem of quantitative evaluation of ideas. Second, we curate and release a benchmark dataset from nearly four thousand manuscript papers with full texts, meticulously designed to train and evaluate the performance of different approaches to this task. Third, we establish a framework for quantifying the value of ideas by employing representations in a specific layer of large language models. Experimental results show that the scores predicted by our method are relatively consistent with those of humans. Our findings suggest that the representations of large language models hold more potential in quantifying the value of ideas than their generative outputs, demonstrating a promising avenue for automating the idea assessment process.<|reference_end|>
|
arxiv
|
@article{xu2024good,
title={Good Idea or Not, Representation of LLM Could Tell},
author={Yi Xu, Bo Xue, Shuqian Sheng, Cheng Deng, Jiaxin Ding, Zanwei Shen,
Luoyi Fu, Xinbing Wang, Chenghu Zhou},
journal={arXiv preprint arXiv:2409.13712},
year={2024},
archivePrefix={arXiv},
eprint={2409.13712},
primaryClass={cs.CL cs.AI}
}
|
xu2024good
|
arxiv-660041
|
2409.13713
|
Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection
|
<|reference_start|>Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection: The World Health Organisation (WHO) revealed approximately 280 million people in the world suffer from depression. Yet, existing studies on early-stage depression detection using machine learning (ML) techniques are limited. Prior studies have applied a single stand-alone algorithm, which is unable to deal with data complexities, prone to overfitting, and limited in generalization. To this end, our paper examined the performance of several ML algorithms for early-stage depression detection using two benchmark social media datasets (D1 and D2). More specifically, we incorporated sentiment indicators to improve our model performance. Our experimental results showed that sentence bidirectional encoder representations from transformers (SBERT) numerical vectors fitted into the stacking ensemble model achieved comparable F1 scores of 69% in the dataset (D1) and 76% in the dataset (D2). Our findings suggest that utilizing sentiment indicators as an additional feature for depression detection yields an improved model performance, and thus, we recommend the development of a depressive term corpus for future work.<|reference_end|>
|
arxiv
|
@article{ogunleye2024sentiment,
title={Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression
Detection},
author={Bayode Ogunleye, Hemlata Sharma, Olamilekan Shobayo},
journal={arXiv preprint arXiv:2409.13713},
year={2024},
doi={10.3390/bdcc8090112},
archivePrefix={arXiv},
eprint={2409.13713},
primaryClass={cs.CL cs.LG math.ST stat.AP stat.TH}
}
|
ogunleye2024sentiment
|
arxiv-660042
|
2409.13714
|
TracrBench: Generating Interpretability Testbeds with Large Language Models
|
<|reference_start|>TracrBench: Generating Interpretability Testbeds with Large Language Models: Achieving a mechanistic understanding of transformer-based language models is an open challenge, especially due to their large number of parameters. Moreover, the lack of ground truth mappings between model weights and their functional roles hinders the effective evaluation of interpretability methods, impeding overall progress. Tracr, a method for generating compiled transformers with inherent ground truth mappings in RASP, has been proposed to address this issue. However, manually creating a large number of models needed for verifying interpretability methods is labour-intensive and time-consuming. In this work, we present a novel approach for generating interpretability test beds using large language models (LLMs) and introduce TracrBench, a novel dataset consisting of 121 manually written and LLM-generated, human-validated RASP programs and their corresponding transformer weights. During this process, we evaluate the ability of frontier LLMs to autonomously generate RASP programs and find that this task poses significant challenges. GPT-4-turbo, with a 20-shot prompt and best-of-5 sampling, correctly implements only 57 out of 101 test programs, necessitating the manual implementation of the remaining programs. With its 121 samples, TracrBench aims to serve as a valuable testbed for evaluating and comparing interpretability methods.<|reference_end|>
|
arxiv
|
@article{thurnherr2024tracrbench:,
title={TracrBench: Generating Interpretability Testbeds with Large Language
Models},
author={Hannes Thurnherr and J'er'emy Scheurer},
journal={arXiv preprint arXiv:2409.13714},
year={2024},
archivePrefix={arXiv},
eprint={2409.13714},
primaryClass={cs.CL cs.AI cs.LG}
}
|
thurnherr2024tracrbench:
|
arxiv-660043
|
2409.13715
|
Introducing MeMo: A Multimodal Dataset for Memory Modelling in Multiparty Conversations
|
<|reference_start|>Introducing MeMo: A Multimodal Dataset for Memory Modelling in Multiparty Conversations: The quality of human social relationships is intricately linked to human memory processes, with memory serving as the foundation for the creation of social bonds. Since human memory is selective, differing recollections of the same events within a group can lead to misunderstandings and misalignments in what is perceived to be common ground in the group. Yet, conversational facilitation systems, aimed at advancing the quality of group interactions, usually focus on tracking users' states within an individual session, ignoring what remains in each participant's memory after the interaction. Conversational memory is the process by which humans encode, retain and retrieve verbal, non-verbal and contextual information from a conversation. Understanding conversational memory can be used as a source of information on the long-term development of social connections within a group. This paper introduces the MeMo corpus, the first conversational dataset annotated with participants' memory retention reports, aimed at facilitating computational modelling of human conversational memory. The MeMo corpus includes 31 hours of small-group discussions on the topic of Covid-19, repeated over the term of 2 weeks. It integrates validated behavioural and perceptual measures, and includes audio, video, and multimodal annotations, offering a valuable resource for studying and modelling conversational memory and group dynamics. By introducing the MeMo corpus, presenting an analysis of its validity, and demonstrating its usefulness for future research, this paper aims to pave the way for future research in conversational memory modelling for intelligent system development.<|reference_end|>
|
arxiv
|
@article{tsfasman2024introducing,
title={Introducing MeMo: A Multimodal Dataset for Memory Modelling in
Multiparty Conversations},
author={Maria Tsfasman, Bernd Dudzik, Kristian Fenech, Andras Lorincz,
Catholijn M. Jonker, Catharine Oertel},
journal={arXiv preprint arXiv:2409.13715},
year={2024},
archivePrefix={arXiv},
eprint={2409.13715},
primaryClass={cs.CL cs.AI cs.HC cs.LG}
}
|
tsfasman2024introducing
|
arxiv-660044
|
2409.13716
|
Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition
|
<|reference_start|>Constrained Multi-Layer Contrastive Learning for Implicit Discourse Relationship Recognition: Previous approaches to the task of implicit discourse relation recognition (IDRR) generally view it as a classification task. Even with pre-trained language models, like BERT and RoBERTa, IDRR still relies on complicated neural networks with multiple intermediate layers to proper capture the interaction between two discourse units. As a result, the outputs of these intermediate layers may have different capability in discriminating instances of different classes. To this end, we propose to adapt a supervised contrastive learning (CL) method, label- and instance-centered CL, to enhance representation learning. Moreover, we propose a novel constrained multi-layer CL approach to properly impose a constraint that the contrastive loss of higher layers should be smaller than that of lower layers. Experimental results on PDTB 2.0 and PDTB 3.0 show that our approach can significantly improve the performance on both multi-class classification and binary classification.<|reference_end|>
|
arxiv
|
@article{wu2024constrained,
title={Constrained Multi-Layer Contrastive Learning for Implicit Discourse
Relationship Recognition},
author={Yiheng Wu, Junhui Li, Muhua Zhu},
journal={arXiv preprint arXiv:2409.13716},
year={2024},
archivePrefix={arXiv},
eprint={2409.13716},
primaryClass={cs.CL}
}
|
wu2024constrained
|
arxiv-660045
|
2409.13717
|
DiVA-DocRE: A Discriminative and Voice-Aware Paradigm for Document-Level Relation Extraction
|
<|reference_start|>DiVA-DocRE: A Discriminative and Voice-Aware Paradigm for Document-Level Relation Extraction: The remarkable capabilities of Large Language Models (LLMs) in text comprehension and generation have revolutionized Information Extraction (IE). One such advancement is in Document-level Relation Triplet Extraction (DocRTE), a critical task in information systems that aims to extract entities and their semantic relationships from documents. However, existing methods are primarily designed for Sentence level Relation Triplet Extraction (SentRTE), which typically handles a limited set of relations and triplet facts within a single sentence. Additionally, some approaches treat relations as candidate choices integrated into prompt templates, resulting in inefficient processing and suboptimal performance when determining the relation elements in triplets. To address these limitations, we introduce a Discriminative and Voice Aware Paradigm DiVA. DiVA involves only two steps: performing document-level relation extraction (DocRE) and then identifying the subject object entities based on the relation. No additional processing is required simply input the document to directly obtain the triplets. This streamlined process more accurately reflects real-world scenarios for triplet extraction. Our innovation lies in transforming DocRE into a discriminative task, where the model pays attention to each relation and to the often overlooked issue of active vs. passive voice within the triplet. Our experiments on the Re-DocRED and DocRED datasets demonstrate state-of-the-art results for the DocRTE task.<|reference_end|>
|
arxiv
|
@article{wu2024diva-docre:,
title={DiVA-DocRE: A Discriminative and Voice-Aware Paradigm for Document-Level
Relation Extraction},
author={Yiheng Wu, Roman Yangarber, Xian Mao},
journal={arXiv preprint arXiv:2409.13717},
year={2024},
archivePrefix={arXiv},
eprint={2409.13717},
primaryClass={cs.CL cs.AI cs.IR}
}
|
wu2024diva-docre:
|
arxiv-660046
|
2409.13720
|
Efficient Classification of Histopathology Images
|
<|reference_start|>Efficient Classification of Histopathology Images: This work addresses how to efficiently classify challenging histopathology images, such as gigapixel whole-slide images for cancer diagnostics with image-level annotation. We use images with annotated tumor regions to identify a set of tumor patches and a set of benign patches in a cancerous slide. Due to the variable nature of region of interest the tumor positive regions may refer to an extreme minority of the pixels. This creates an important problem during patch-level classification, where the majority of patches from an image labeled as 'cancerous' are actually tumor-free. This problem is different from semantic segmentation which associates a label to every pixel in an image, because after patch extraction we are only dealing with patch-level labels.Most existing approaches address the data imbalance issue by mitigating the data shortage in minority classes in order to prevent the model from being dominated by the majority classes. These methods include data re-sampling, loss re-weighting, margin modification, and data augmentation. In this work, we mitigate the patch-level class imbalance problem by taking a divide-and-conquer approach. First, we partition the data into sub-groups, and define three separate classification sub-problems based on these data partitions. Then, using an information-theoretic cluster-based sampling of deep image patch features, we sample discriminative patches from the sub-groups. Using these sampled patches, we build corresponding deep models to solve the new classification sub-problems. Finally, we integrate information learned from the respective models to make a final decision on the patches. Our result shows that the proposed approach can perform competitively using a very low percentage of the available patches in a given whole-slide image.<|reference_end|>
|
arxiv
|
@article{nouyed2024efficient,
title={Efficient Classification of Histopathology Images},
author={Mohammad Iqbal Nouyed, Mary-Anne Hartley, Gianfranco Doretto, Donald
A. Adjeroh},
journal={arXiv preprint arXiv:2409.13720},
year={2024},
archivePrefix={arXiv},
eprint={2409.13720},
primaryClass={eess.IV cs.CV}
}
|
nouyed2024efficient
|
arxiv-660047
|
2409.13721
|
LegiLM: A Fine-Tuned Legal Language Model for Data Compliance
|
<|reference_start|>LegiLM: A Fine-Tuned Legal Language Model for Data Compliance: Ensuring compliance with international data protection standards for privacy and data security is a crucial but complex task, often requiring substantial legal expertise. This paper introduces LegiLM, a novel legal language model specifically tailored for consulting on data or information compliance. LegiLM leverages a pre-trained GDPR Fines dataset and has been fine-tuned to automatically assess whether particular actions or events breach data security and privacy regulations. By incorporating a specialized dataset that includes global data protection laws, meticulously annotated policy documents, and relevant privacy policies, LegiLM is optimized for addressing data compliance challenges. The model integrates advanced legal reasoning methods and information retrieval enhancements to enhance accuracy and reliability in practical legal consulting scenarios. Our evaluation using a custom benchmark dataset demonstrates that LegiLM excels in detecting data regulation breaches, offering sound legal justifications, and recommending necessary compliance modifications, setting a new benchmark for AI-driven legal compliance solutions. Our resources are publicly available at https://github.com/DAOLegalAI/LegiLM<|reference_end|>
|
arxiv
|
@article{zhu2024legilm:,
title={LegiLM: A Fine-Tuned Legal Language Model for Data Compliance},
author={Linkai Zhu, Lu Yang, Chaofan Li, Shanwen Hu, Lu Liu, Bin Yin},
journal={arXiv preprint arXiv:2409.13721},
year={2024},
archivePrefix={arXiv},
eprint={2409.13721},
primaryClass={cs.CL}
}
|
zhu2024legilm:
|
arxiv-660048
|
2409.13723
|
Explainable Malware Analysis: Concepts, Approaches and Challenges
|
<|reference_start|>Explainable Malware Analysis: Concepts, Approaches and Challenges: Machine learning (ML) has seen exponential growth in recent years, finding applications in various domains such as finance, medicine, and cybersecurity. Malware remains a significant threat to modern computing, frequently used by attackers to compromise systems. While numerous machine learning-based approaches for malware detection achieve high performance, they often lack transparency and fail to explain their predictions. This is a critical drawback in malware analysis, where understanding the rationale behind detections is essential for security analysts to verify and disseminate information. Explainable AI (XAI) addresses this issue by maintaining high accuracy while producing models that provide clear, understandable explanations for their decisions. In this survey, we comprehensively review the current state-of-the-art ML-based malware detection techniques and popular XAI approaches. Additionally, we discuss research implementations and the challenges of explainable malware analysis. This theoretical survey serves as an entry point for researchers interested in XAI applications in malware detection. By analyzing recent advancements in explainable malware analysis, we offer a broad overview of the progress in this field, positioning our work as the first to extensively cover XAI methods for malware classification and detection.<|reference_end|>
|
arxiv
|
@article{manthena2024explainable,
title={Explainable Malware Analysis: Concepts, Approaches and Challenges},
author={Harikha Manthena, Shaghayegh Shajarian, Jeffrey Kimmell, Mahmoud
Abdelsalam, Sajad Khorsandroo, and Maanak Gupta},
journal={arXiv preprint arXiv:2409.13723},
year={2024},
archivePrefix={arXiv},
eprint={2409.13723},
primaryClass={cs.CR cs.AI}
}
|
manthena2024explainable
|
arxiv-660049
|
2409.13724
|
Logically Consistent Language Models via Neuro-Symbolic Integration
|
<|reference_start|>Logically Consistent Language Models via Neuro-Symbolic Integration: Large language models (LLMs) are a promising venue for natural language understanding and generation. However, current LLMs are far from reliable: they are prone to generating non-factual information and, more crucially, to contradicting themselves when prompted to reason about relations between entities of the world. These problems are currently addressed with large scale fine-tuning or by delegating reasoning to external tools. In this work, we strive for a middle ground and introduce a loss based on neuro-symbolic reasoning that teaches an LLM to be logically consistent with an external set of facts and rules and improves self-consistency even when the LLM is fine-tuned on a limited set of facts. Our approach also allows to easily combine multiple logical constraints at once in a principled way, delivering LLMs that are more consistent w.r.t. all constraints and improve over several baselines w.r.t. a given constraint. Moreover, our method allows LLMs to extrapolate to unseen but semantically similar factual knowledge, represented in unseen datasets, more systematically.<|reference_end|>
|
arxiv
|
@article{calanzone2024logically,
title={Logically Consistent Language Models via Neuro-Symbolic Integration},
author={Diego Calanzone, Stefano Teso, Antonio Vergari},
journal={arXiv preprint arXiv:2409.13724},
year={2024},
archivePrefix={arXiv},
eprint={2409.13724},
primaryClass={cs.CL cs.AI cs.LG}
}
|
calanzone2024logically
|
arxiv-660050
|
2409.13725
|
Identity-related Speech Suppression in Generative AI Content Moderation
|
<|reference_start|>Identity-related Speech Suppression in Generative AI Content Moderation: Automated content moderation has long been used to help identify and filter undesired user-generated content online. Generative AI systems now use such filters to keep undesired generated content from being created by or shown to users. From classrooms to Hollywood, as generative AI is increasingly used for creative or expressive text generation, whose stories will these technologies allow to be told, and whose will they suppress? In this paper, we define and introduce measures of speech suppression, focusing on speech related to different identity groups incorrectly filtered by a range of content moderation APIs. Using both short-form, user-generated datasets traditional in content moderation and longer generative AI-focused data, including two datasets we introduce in this work, we create a benchmark for measurement of speech suppression for nine identity groups. Across one traditional and four generative AI-focused automated content moderation services tested, we find that identity-related speech is more likely to be incorrectly suppressed than other speech except in the cases of a few non-marginalized groups. Additionally, we find differences between APIs in their abilities to correctly moderate generative AI content.<|reference_end|>
|
arxiv
|
@article{anigboro2024identity-related,
title={Identity-related Speech Suppression in Generative AI Content Moderation},
author={Oghenefejiro Isaacs Anigboro, Charlie M. Crawford, Dana"e Metaxa,
Sorelle A. Friedler},
journal={arXiv preprint arXiv:2409.13725},
year={2024},
archivePrefix={arXiv},
eprint={2409.13725},
primaryClass={cs.CL cs.CY cs.HC}
}
|
anigboro2024identity-related
|
arxiv-660051
|
2409.13726
|
Multilingual Dyadic Interaction Corpus NoXi+J: Toward Understanding Asian-European Non-verbal Cultural Characteristics and their Influences on Engagement
|
<|reference_start|>Multilingual Dyadic Interaction Corpus NoXi+J: Toward Understanding Asian-European Non-verbal Cultural Characteristics and their Influences on Engagement: Non-verbal behavior is a central challenge in understanding the dynamics of a conversation and the affective states between interlocutors arising from the interaction. Although psychological research has demonstrated that non-verbal behaviors vary across cultures, limited computational analysis has been conducted to clarify these differences and assess their impact on engagement recognition. To gain a greater understanding of engagement and non-verbal behaviors among a wide range of cultures and language spheres, in this study we conduct a multilingual computational analysis of non-verbal features and investigate their role in engagement and engagement prediction. To achieve this goal, we first expanded the NoXi dataset, which contains interaction data from participants living in France, Germany, and the United Kingdom, by collecting session data of dyadic conversations in Japanese and Chinese, resulting in the enhanced dataset NoXi+J. Next, we extracted multimodal non-verbal features, including speech acoustics, facial expressions, backchanneling and gestures, via various pattern recognition techniques and algorithms. Then, we conducted a statistical analysis of listening behaviors and backchannel patterns to identify culturally dependent and independent features in each language and common features among multiple languages. These features were also correlated with the engagement shown by the interlocutors. Finally, we analyzed the influence of cultural differences in the input features of LSTM models trained to predict engagement for five language datasets. A SHAP analysis combined with transfer learning confirmed a considerable correlation between the importance of input features for a language set and the significant cultural characteristics analyzed.<|reference_end|>
|
arxiv
|
@article{funk2024multilingual,
title={Multilingual Dyadic Interaction Corpus NoXi+J: Toward Understanding
Asian-European Non-verbal Cultural Characteristics and their Influences on
Engagement},
author={Marius Funk, Shogo Okada, Elisabeth Andr'e},
journal={arXiv preprint arXiv:2409.13726},
year={2024},
doi={10.1145/3678957.3685757},
archivePrefix={arXiv},
eprint={2409.13726},
primaryClass={cs.CL cs.AI cs.HC cs.LG}
}
|
funk2024multilingual
|
arxiv-660052
|
2409.13727
|
Classification performance and reproducibility of GPT-4 omni for information extraction from veterinary electronic health records
|
<|reference_start|>Classification performance and reproducibility of GPT-4 omni for information extraction from veterinary electronic health records: Large language models (LLMs) can extract information from veterinary electronic health records (EHRs), but performance differences between models, the effect of temperature settings, and the influence of text ambiguity have not been previously evaluated. This study addresses these gaps by comparing the performance of GPT-4 omni (GPT-4o) and GPT-3.5 Turbo under different conditions and investigating the relationship between human interobserver agreement and LLM errors. The LLMs and five humans were tasked with identifying six clinical signs associated with Feline chronic enteropathy in 250 EHRs from a veterinary referral hospital. At temperature 0, the performance of GPT-4o compared to the majority opinion of human respondents, achieved 96.9% sensitivity (interquartile range [IQR] 92.9-99.3%), 97.6% specificity (IQR 96.5-98.5%), 80.7% positive predictive value (IQR 70.8-84.6%), 99.5% negative predictive value (IQR 99.0-99.9%), 84.4% F1 score (IQR 77.3-90.4%), and 96.3% balanced accuracy (IQR 95.0-97.9%). The performance of GPT-4o was significantly better than that of its predecessor, GPT-3.5 Turbo, particularly with respect to sensitivity where GPT-3.5 Turbo only achieved 81.7% (IQR 78.9-84.8%). Adjusting the temperature for GPT-4o did not significantly impact classification performance. GPT-4o demonstrated greater reproducibility than human pairs regardless of temperature, with an average Cohen's kappa of 0.98 (IQR 0.98-0.99) at temperature 0 compared to 0.8 (IQR 0.78-0.81) for humans. Most GPT-4o errors occurred in instances where humans disagreed (35/43 errors, 81.4%), suggesting that these errors were more likely caused by ambiguity of the EHR than explicit model faults. Using GPT-4o to automate information extraction from veterinary EHRs is a viable alternative to manual extraction.<|reference_end|>
|
arxiv
|
@article{wulcan2024classification,
title={Classification performance and reproducibility of GPT-4 omni for
information extraction from veterinary electronic health records},
author={Judit M Wulcan, Kevin L Jacques, Mary Ann Lee, Samantha L Kovacs,
Nicole Dausend, Lauren E Prince, Jonatan Wulcan, Sina Marsilio, Stefan M
Keller},
journal={arXiv preprint arXiv:2409.13727},
year={2024},
archivePrefix={arXiv},
eprint={2409.13727},
primaryClass={cs.CL cs.IR}
}
|
wulcan2024classification
|
arxiv-660053
|
2409.13728
|
Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts
|
<|reference_start|>Rule Extrapolation in Language Models: A Study of Compositional Generalization on OOD Prompts: LLMs show remarkable emergent abilities, such as inferring concepts from presumably out-of-distribution prompts, known as in-context learning. Though this success is often attributed to the Transformer architecture, our systematic understanding is limited. In complex real-world data sets, even defining what is out-of-distribution is not obvious. To better understand the OOD behaviour of autoregressive LLMs, we focus on formal languages, which are defined by the intersection of rules. We define a new scenario of OOD compositional generalization, termed rule extrapolation. Rule extrapolation describes OOD scenarios, where the prompt violates at least one rule. We evaluate rule extrapolation in formal languages with varying complexity in linear and recurrent architectures, the Transformer, and state space models to understand the architectures' influence on rule extrapolation. We also lay the first stones of a normative theory of rule extrapolation, inspired by the Solomonoff prior in algorithmic information theory.<|reference_end|>
|
arxiv
|
@article{mészáros2024rule,
title={Rule Extrapolation in Language Models: A Study of Compositional
Generalization on OOD Prompts},
author={Anna M'esz'aros, Szilvia Ujv'ary, Wieland Brendel, Patrik
Reizinger, Ferenc Husz'ar},
journal={arXiv preprint arXiv:2409.13728},
year={2024},
archivePrefix={arXiv},
eprint={2409.13728},
primaryClass={cs.CL cs.LG stat.ML}
}
|
mészáros2024rule
|
arxiv-660054
|
2409.13729
|
MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large Language Model
|
<|reference_start|>MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large Language Model: Large language models (LLMs) have demonstrated significant capabilities in mathematical reasoning, particularly with text-based mathematical problems. However, current multi-modal large language models (MLLMs), especially those specialized in mathematics, tend to focus predominantly on solving geometric problems but ignore the diversity of visual information available in other areas of mathematics. Moreover, the geometric information for these specialized mathematical MLLMs is derived from several public datasets, which are typically limited in diversity and complexity. To address these limitations, we aim to construct a fine-tuning dataset named MathVL, and develop a series of specialized mathematical MLLMs termed MathGLM-Vision by conducting Supervised Fine-Tuning (SFT) on MathVL with various parameter-scale backbones. To extensively evaluate the effectiveness of MathGLM-Vision, we conduct experiments on several public benchmarks and our curated MathVL-test consisting of 2,000 problems. Experimental results demonstrate that MathGLM-Vision achieves significant improvements compared with some existing models, including backbone models and open-source mathematical MLLMs. These findings indicate the importance of diversity dataset in enhancing the mathematical reasoning abilities of MLLMs.<|reference_end|>
|
arxiv
|
@article{yang2024mathglm-vision:,
title={MathGLM-Vision: Solving Mathematical Problems with Multi-Modal Large
Language Model},
author={Zhen Yang, Jinhao Chen, Zhengxiao Du, Wenmeng Yu, Weihan Wang, Wenyi
Hong, Zhihuan Jiang, Bin Xu, Yuxiao Dong, Jie Tang},
journal={arXiv preprint arXiv:2409.13729},
year={2024},
archivePrefix={arXiv},
eprint={2409.13729},
primaryClass={cs.CL cs.AI}
}
|
yang2024mathglm-vision:
|
arxiv-660055
|
2409.13730
|
VisScience: An Extensive Benchmark for Evaluating K12 Educational Multi-modal Scientific Reasoning
|
<|reference_start|>VisScience: An Extensive Benchmark for Evaluating K12 Educational Multi-modal Scientific Reasoning: Multi-modal large language models (MLLMs) have demonstrated promising capabilities across various tasks by integrating textual and visual information to achieve visual understanding in complex scenarios. Despite the availability of several benchmarks aims to evaluating MLLMs in tasks from visual question answering to complex problem-solving, most focus predominantly on mathematics or general visual understanding tasks. This reveals a critical gap in current benchmarks, which often overlook the inclusion of other key scientific disciplines such as physics and chemistry. To address this gap, we meticulously construct a comprehensive benchmark, named VisScience, which is utilized to assess the multi-modal scientific reasoning across the three disciplines of mathematics, physics, and chemistry. This benchmark comprises 3,000 questions drawn from K12 education - spanning elementary school through high school - equally distributed across three disciplines, with 1,000 questions per discipline. The questions within VisScience span 21 distinct subjects and are categorized into five difficulty levels, offering a broad spectrum of topics within each discipline. With VisScience, we present a detailed evaluation of the performance of 25 representative MLLMs in scientific reasoning. Experimental results demonstrate that closed-source MLLMs generally outperform open-source models. The best performance observed include a 53.4\% accuracy in mathematics by Claude3.5-Sonnet, 38.2\% in physics by GPT-4o, and 47.0\% in chemistry by Gemini-1.5-Pro. These results underscore the strengths and limitations of MLLMs, suggesting areas for future improvement and highlighting the importance of developing models that can effectively handle the diverse demands of multi-modal scientific reasoning.<|reference_end|>
|
arxiv
|
@article{jiang2024visscience:,
title={VisScience: An Extensive Benchmark for Evaluating K12 Educational
Multi-modal Scientific Reasoning},
author={Zhihuan Jiang, Zhen Yang, Jinhao Chen, Zhengxiao Du, Weihan Wang, Bin
Xu, Yuxiao Dong, Jie Tang},
journal={arXiv preprint arXiv:2409.13730},
year={2024},
archivePrefix={arXiv},
eprint={2409.13730},
primaryClass={cs.AI cs.CL}
}
|
jiang2024visscience:
|
arxiv-660056
|
2409.13731
|
KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation
|
<|reference_start|>KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation: The recently developed retrieval-augmented generation (RAG) technology has enabled the efficient construction of domain-specific applications. However, it also has limitations, including the gap between vector similarity and the relevance of knowledge reasoning, as well as insensitivity to knowledge logic, such as numerical values, temporal relations, expert rules, and others, which hinder the effectiveness of professional knowledge services. In this work, we introduce a professional domain knowledge service framework called Knowledge Augmented Generation (KAG). KAG is designed to address the aforementioned challenges with the motivation of making full use of the advantages of knowledge graph(KG) and vector retrieval, and to improve generation and reasoning performance by bidirectionally enhancing large language models (LLMs) and KGs through five key aspects: (1) LLM-friendly knowledge representation, (2) mutual-indexing between knowledge graphs and original chunks, (3) logical-form-guided hybrid reasoning engine, (4) knowledge alignment with semantic reasoning, and (5) model capability enhancement for KAG. We compared KAG with existing RAG methods in multihop question answering and found that it significantly outperforms state-of-theart methods, achieving a relative improvement of 19.6% on 2wiki and 33.5% on hotpotQA in terms of F1 score. We have successfully applied KAG to two professional knowledge Q&A tasks of Ant Group, including E-Government Q&A and E-Health Q&A, achieving significant improvement in professionalism compared to RAG methods.<|reference_end|>
|
arxiv
|
@article{liang2024kag:,
title={KAG: Boosting LLMs in Professional Domains via Knowledge Augmented
Generation},
author={Lei Liang, Mengshu Sun, Zhengke Gui, Zhongshu Zhu, Zhouyu Jiang, Ling
Zhong, Yuan Qu, Peilong Zhao, Zhongpu Bo, Jin Yang, Huaidong Xiong, Lin Yuan,
Jun Xu, Zaoyang Wang, Zhiqiang Zhang, Wen Zhang, Huajun Chen, Wenguang Chen
and Jun Zhou},
journal={arXiv preprint arXiv:2409.13731},
year={2024},
archivePrefix={arXiv},
eprint={2409.13731},
primaryClass={cs.CL cs.AI}
}
|
liang2024kag:
|
arxiv-660057
|
2409.13732
|
TopoChat: Enhancing Topological Materials Retrieval With Large Language Model and Multi-Source Knowledge
|
<|reference_start|>TopoChat: Enhancing Topological Materials Retrieval With Large Language Model and Multi-Source Knowledge: Large language models (LLMs), such as ChatGPT, have demonstrated impressive performance in the text generation task, showing the ability to understand and respond to complex instructions. However, the performance of naive LLMs in speciffc domains is limited due to the scarcity of domain-speciffc corpora and specialized training. Moreover, training a specialized large-scale model necessitates signiffcant hardware resources, which restricts researchers from leveraging such models to drive advances. Hence, it is crucial to further improve and optimize LLMs to meet speciffc domain demands and enhance their scalability. Based on the condensed matter data center, we establish a material knowledge graph (MaterialsKG) and integrate it with literature. Using large language models and prompt learning, we develop a specialized dialogue system for topological materials called TopoChat. Compared to naive LLMs, TopoChat exhibits superior performance in structural and property querying, material recommendation, and complex relational reasoning. This system enables efffcient and precise retrieval of information and facilitates knowledge interaction, thereby encouraging the advancement on the ffeld of condensed matter materials.<|reference_end|>
|
arxiv
|
@article{xu2024topochat:,
title={TopoChat: Enhancing Topological Materials Retrieval With Large Language
Model and Multi-Source Knowledge},
author={HuangChao Xu, Baohua Zhang, Zhong Jin, Tiannian Zhu, Quansheng Wu and
Hongming Weng},
journal={arXiv preprint arXiv:2409.13732},
year={2024},
archivePrefix={arXiv},
eprint={2409.13732},
primaryClass={cs.CL cond-mat.mtrl-sci cs.LG}
}
|
xu2024topochat:
|
arxiv-660058
|
2409.13733
|
RNR: Teaching Large Language Models to Follow Roles and Rules
|
<|reference_start|>RNR: Teaching Large Language Models to Follow Roles and Rules: Instruction fine-tuning (IFT) elicits instruction following capabilities and steers the behavior of large language models (LLMs) via supervised learning. However, existing models trained on open-source IFT datasets only have the ability to follow instructions from users, and often fail to follow complex role and rules specified by developers, a.k.a. system prompts. The ability to follow these roles and rules is essential for deployment, as it ensures that the model safely interacts with users within developer defined guidelines. To improve such role and rule following ability, we propose \model, an automated data generation pipeline that generates diverse roles and rules from existing IFT instructions, along with corresponding responses. This data can then be used to train models that follow complex system prompts. The models are evaluated on our newly created benchmarks for role and rule following ability, as well as standard instruction-following benchmarks and general NLP tasks. Our framework significantly improves role and rule following capability in LLMs, as evidenced by over 25% increase in pass-rate on rule adherence, i.e. following all requirements, in our experiments with the Alpaca and Ultrachat datasets. Moreover, our models achieves this increase without any regression on popular instruction following benchmarks.<|reference_end|>
|
arxiv
|
@article{wang2024rnr:,
title={RNR: Teaching Large Language Models to Follow Roles and Rules},
author={Kuan Wang, Alexander Bukharin, Haoming Jiang, Qingyu Yin, Zhengyang
Wang, Tuo Zhao, Jingbo Shang, Chao Zhang, Bing Yin, Xian Li, Jianshu Chen,
Shiyang Li},
journal={arXiv preprint arXiv:2409.13733},
year={2024},
archivePrefix={arXiv},
eprint={2409.13733},
primaryClass={cs.CL cs.AI cs.HC}
}
|
wang2024rnr:
|
arxiv-660059
|
2409.13734
|
Enhancing Kurdish Text-to-Speech with Native Corpus Training: A High-Quality WaveGlow Vocoder Approach
|
<|reference_start|>Enhancing Kurdish Text-to-Speech with Native Corpus Training: A High-Quality WaveGlow Vocoder Approach: The ability to synthesize spoken language from text has greatly facilitated access to digital content with the advances in text-to-speech technology. However, effective TTS development for low-resource languages, such as Central Kurdish (CKB), still faces many challenges due mainly to the lack of linguistic information and dedicated resources. In this paper, we improve the Kurdish TTS system based on Tacotron by training the Kurdish WaveGlow vocoder on a 21-hour central Kurdish speech corpus instead of using a pre-trained English vocoder WaveGlow. Vocoder training on the target language corpus is required to accurately and fluently adapt phonetic and prosodic changes in Kurdish language. The effectiveness of these enhancements is that our model is significantly better than the baseline system with English pretrained models. In particular, our adaptive WaveGlow model achieves an impressive MOS of 4.91, which sets a new benchmark for Kurdish speech synthesis. On one hand, this study empowers the advanced features of the TTS system for Central Kurdish, and on the other hand, it opens the doors for other dialects in Kurdish and other related languages to further develop.<|reference_end|>
|
arxiv
|
@article{abdullah2024enhancing,
title={Enhancing Kurdish Text-to-Speech with Native Corpus Training: A
High-Quality WaveGlow Vocoder Approach},
author={Abdulhady Abas Abdullah, Sabat Salih Muhamad, Hadi Veisi},
journal={arXiv preprint arXiv:2409.13734},
year={2024},
archivePrefix={arXiv},
eprint={2409.13734},
primaryClass={cs.CL cs.SD eess.AS}
}
|
abdullah2024enhancing
|
arxiv-660060
|
2409.13735
|
Analysis of Socially Unacceptable Discourse with Zero-shot Learning
|
<|reference_start|>Analysis of Socially Unacceptable Discourse with Zero-shot Learning: Socially Unacceptable Discourse (SUD) analysis is crucial for maintaining online positive environments. We investigate the effectiveness of Entailment-based zero-shot text classification (unsupervised method) for SUD detection and characterization by leveraging pre-trained transformer models and prompting techniques. The results demonstrate good generalization capabilities of these models to unseen data and highlight the promising nature of this approach for generating labeled datasets for the analysis and characterization of extremist narratives. The findings of this research contribute to the development of robust tools for studying SUD and promoting responsible communication online.<|reference_end|>
|
arxiv
|
@article{ghilene2024analysis,
title={Analysis of Socially Unacceptable Discourse with Zero-shot Learning},
author={Rayane Ghilene, Dimitra Niaouri, Michele Linardi, and Julien Longhi},
journal={International Conference on CMC and Social Media Corpora for the
Humanities, University C{\^o}te d'Azur, France, 2024, Sep 2024, Nice
(FRANCE), France},
year={2024},
archivePrefix={arXiv},
eprint={2409.13735},
primaryClass={cs.CL}
}
|
ghilene2024analysis
|
arxiv-660061
|
2409.13738
|
NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methods
|
<|reference_start|>NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methods: This literature review studies the field of automated process extraction, i.e., transforming textual descriptions into structured processes using Natural Language Processing (NLP). We found that Machine Learning (ML) / Deep Learning (DL) methods are being increasingly used for the NLP component. In some cases, they were chosen for their suitability towards process extraction, and results show that they can outperform classic rule-based methods. We also found a paucity of gold-standard, scalable annotated datasets, which currently hinders objective evaluations as well as the training or fine-tuning of ML / DL methods. Finally, we discuss preliminary work on the application of LLMs for automated process extraction, as well as promising developments in this field.<|reference_end|>
|
arxiv
|
@article{van woensel2024nlp4pbm:,
title={NLP4PBM: A Systematic Review on Process Extraction using Natural
Language Processing with Rule-based, Machine and Deep Learning Methods},
author={William Van Woensel and Soroor Motie},
journal={arXiv preprint arXiv:2409.13738},
year={2024},
archivePrefix={arXiv},
eprint={2409.13738},
primaryClass={cs.CL}
}
|
van woensel2024nlp4pbm:
|
arxiv-660062
|
2409.13739
|
Table-to-Text Generation with Pretrained Diffusion Models
|
<|reference_start|>Table-to-Text Generation with Pretrained Diffusion Models: Diffusion models have demonstrated significant potential in achieving state-of-the-art performance across various text generation tasks. In this systematic study, we investigate their application to the table-to-text problem by adapting the diffusion model to the task and conducting an in-depth analysis. Our experiments cover multiple aspects of diffusion models training. We explore sampling strategy influence by inducing recent diffusion model accelerator DPM-Solver++ into our core model. We have tested different prediction aggregation methods, like ROVER and Minimum Bayes-Risk (MBR). Our studies cover the impact of the pre-training phase in diffusion models and the generation length constraints influence. We also have compared diffusion model generation with auto-regressive text-to-text models with different temperature settings for diversity evaluation. Our key observation is that diffusion models demonstrate the balance between quality and diversity while auto-regressive text-to-text models are not successful at handling both at the same time. Furthermore, we found out that to achieve the highest quality possible, it is preferable to use a regular sampler with the strictest length constraint to create multiple samples, and then use MBR to aggregate the predictions. However, if you are prepared to give up high level of diversity and to accelerate the process, you can also utilize a fast sampler DPM-Solver++. Our findings reveal that diffusion models achieve comparable results in the table-to-text domain, highlighting their viability in the table-to-text challenge as a promising research direction.<|reference_end|>
|
arxiv
|
@article{krylov2024table-to-text,
title={Table-to-Text Generation with Pretrained Diffusion Models},
author={Aleksei S. Krylov, Oleg D. Somov},
journal={IEEE Access, vol. 12, pp. 110517-110525, 2024},
year={2024},
doi={10.1109/ACCESS.2024.3440006},
archivePrefix={arXiv},
eprint={2409.13739},
primaryClass={cs.CL}
}
|
krylov2024table-to-text
|
arxiv-660063
|
2409.13740
|
Language agents achieve superhuman synthesis of scientific knowledge
|
<|reference_start|>Language agents achieve superhuman synthesis of scientific knowledge: Language models are known to hallucinate incorrect information, and it is unclear if they are sufficiently accurate and reliable for use in scientific research. We developed a rigorous human-AI comparison methodology to evaluate language model agents on real-world literature search tasks covering information retrieval, summarization, and contradiction detection tasks. We show that PaperQA2, a frontier language model agent optimized for improved factuality, matches or exceeds subject matter expert performance on three realistic literature research tasks without any restrictions on humans (i.e., full access to internet, search tools, and time). PaperQA2 writes cited, Wikipedia-style summaries of scientific topics that are significantly more accurate than existing, human-written Wikipedia articles. We also introduce a hard benchmark for scientific literature research called LitQA2 that guided design of PaperQA2, leading to it exceeding human performance. Finally, we apply PaperQA2 to identify contradictions within the scientific literature, an important scientific task that is challenging for humans. PaperQA2 identifies 2.34 +/- 1.99 contradictions per paper in a random subset of biology papers, of which 70% are validated by human experts. These results demonstrate that language model agents are now capable of exceeding domain experts across meaningful tasks on scientific literature.<|reference_end|>
|
arxiv
|
@article{skarlinski2024language,
title={Language agents achieve superhuman synthesis of scientific knowledge},
author={Michael D. Skarlinski, Sam Cox, Jon M. Laurent, James D. Braza,
Michaela Hinks, Michael J. Hammerling, Manvitha Ponnapati, Samuel G.
Rodriques, and Andrew D. White},
journal={arXiv preprint arXiv:2409.13740},
year={2024},
archivePrefix={arXiv},
eprint={2409.13740},
primaryClass={cs.CL cs.AI cs.IR physics.soc-ph}
}
|
skarlinski2024language
|
arxiv-660064
|
2409.13741
|
Knowing When to Ask -- Bridging Large Language Models and Data
|
<|reference_start|>Knowing When to Ask -- Bridging Large Language Models and Data: Large Language Models (LLMs) are prone to generating factually incorrect information when responding to queries that involve numerical and statistical data or other timely facts. In this paper, we present an approach for enhancing the accuracy of LLMs by integrating them with Data Commons, a vast, open-source repository of public statistics from trusted organizations like the United Nations (UN), Center for Disease Control and Prevention (CDC) and global census bureaus. We explore two primary methods: Retrieval Interleaved Generation (RIG), where the LLM is trained to produce natural language queries to retrieve data from Data Commons, and Retrieval Augmented Generation (RAG), where relevant data tables are fetched from Data Commons and used to augment the LLM's prompt. We evaluate these methods on a diverse set of queries, demonstrating their effectiveness in improving the factual accuracy of LLM outputs. Our work represents an early step towards building more trustworthy and reliable LLMs that are grounded in verifiable statistical data and capable of complex factual reasoning.<|reference_end|>
|
arxiv
|
@article{radhakrishnan2024knowing,
title={Knowing When to Ask -- Bridging Large Language Models and Data},
author={Prashanth Radhakrishnan, Jennifer Chen, Bo Xu, Prem Ramaswami, Hannah
Pho, Adriana Olmos, James Manyika, R. V. Guha},
journal={arXiv preprint arXiv:2409.13741},
year={2024},
archivePrefix={arXiv},
eprint={2409.13741},
primaryClass={cs.CL cs.IR}
}
|
radhakrishnan2024knowing
|
arxiv-660065
|
2409.13742
|
Distinguishability Investigation on Longa's Atomic Patterns when used as a Basis for Implementing Elliptic Curve Scalar Multiplication Algorithms
|
<|reference_start|>Distinguishability Investigation on Longa's Atomic Patterns when used as a Basis for Implementing Elliptic Curve Scalar Multiplication Algorithms: In the evolving landscape of cryptographic security, the robustness of Elliptic Curve Cryptography (ECC) against side-channel analysis (SCA) attacks is of paramount importance due to the widespread use of ECC and the growing sophistication of SCAs. This thesis delves into the investigation of Longa's atomic patterns applied within Elliptic Curve scalar multiplication algorithms, assessing their resistance to horizontal SCAs. The research employs these atomic patterns in practical implementation on a microcontroller (Texas Instruments Launchpad F28379 board) using the open-source cryptographic library FLECC in C. In our analysis, we only focused on the distinguishability of the first atomic block in the Elliptic Curve point doubling and point addition patterns. Due to various technical limitations, we were unable to determine significant differences in the execution time and the shapes of the atomic blocks. Further investigations of the SCA-resistance can be performed based on this work. A significant contribution of this work is the identification and correction of several discrepancies in Longa's original atomic patterns. This thesis marks the first practical implementation of Longa's patterns, extending the theoretical research into empirical analysis.<|reference_end|>
|
arxiv
|
@article{li2024distinguishability,
title={Distinguishability Investigation on Longa's Atomic Patterns when used as
a Basis for Implementing Elliptic Curve Scalar Multiplication Algorithms},
author={Sze Hei Li},
journal={arXiv preprint arXiv:2409.13742},
year={2024},
archivePrefix={arXiv},
eprint={2409.13742},
primaryClass={cs.CR}
}
|
li2024distinguishability
|
arxiv-660066
|
2409.13743
|
Effect of Clinical History on Predictive Model Performance for Renal Complications of Diabetes
|
<|reference_start|>Effect of Clinical History on Predictive Model Performance for Renal Complications of Diabetes: Diabetes is a chronic disease characterised by a high risk of developing diabetic nephropathy, which, in turn, is the leading cause of end-stage chronic kidney disease. The early identification of individuals at heightened risk of such complications or their exacerbation can be of paramount importance to set a correct course of treatment. In the present work, from the data collected in the DARWIN-Renal (DApagliflozin Real-World evIdeNce-Renal) study, a nationwide multicentre retrospective real-world study, we develop an array of logistic regression models to predict, over different prediction horizons, the crossing of clinically relevant glomerular filtration rate (eGFR) thresholds for patients with diabetes by means of variables associated with demographic, anthropometric, laboratory, pathology, and therapeutic data. In doing so, we investigate the impact of information coming from patient's past visits on the model's predictive performance, coupled with an analysis of feature importance through the Boruta algorithm. Our models yield very good performance (AUROC as high as 0.98). We also show that the introduction of information from patient's past visits leads to improved model performance of up to 4%. The usefulness of past information is further corroborated by a feature importance analysis.<|reference_end|>
|
arxiv
|
@article{cas2024effect,
title={Effect of Clinical History on Predictive Model Performance for Renal
Complications of Diabetes},
author={Davide Dei Cas, Barbara Di Camillo, Gian Paolo Fadini, Giovanni
Sparacino, Enrico Longato},
journal={arXiv preprint arXiv:2409.13743},
year={2024},
archivePrefix={arXiv},
eprint={2409.13743},
primaryClass={q-bio.QM cs.LG}
}
|
cas2024effect
|
arxiv-660067
|
2409.13744
|
A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models
|
<|reference_start|>A Simplified Retriever to Improve Accuracy of Phenotype Normalizations by Large Language Models: Large language models (LLMs) have shown improved accuracy in phenotype term normalization tasks when augmented with retrievers that suggest candidate normalizations based on term definitions. In this work, we introduce a simplified retriever that enhances LLM accuracy by searching the Human Phenotype Ontology (HPO) for candidate matches using contextual word embeddings from BioBERT without the need for explicit term definitions. Testing this method on terms derived from the clinical synopses of Online Mendelian Inheritance in Man (OMIM), we demonstrate that the normalization accuracy of a state-of-the-art LLM increases from a baseline of 62.3% without augmentation to 90.3% with retriever augmentation. This approach is potentially generalizable to other biomedical term normalization tasks and offers an efficient alternative to more complex retrieval methods.<|reference_end|>
|
arxiv
|
@article{hier2024a,
title={A Simplified Retriever to Improve Accuracy of Phenotype Normalizations
by Large Language Models},
author={Daniel B. Hier and Thanh Son Do and Tayo Obafemi-Ajayi},
journal={arXiv preprint arXiv:2409.13744},
year={2024},
archivePrefix={arXiv},
eprint={2409.13744},
primaryClass={cs.CL cs.AI cs.IR}
}
|
hier2024a
|
arxiv-660068
|
2409.13745
|
Context-Aware Membership Inference Attacks against Pre-trained Large Language Models
|
<|reference_start|>Context-Aware Membership Inference Attacks against Pre-trained Large Language Models: Prior Membership Inference Attacks (MIAs) on pre-trained Large Language Models (LLMs), adapted from classification model attacks, fail due to ignoring the generative process of LLMs across token sequences. In this paper, we present a novel attack that adapts MIA statistical tests to the perplexity dynamics of subsequences within a data point. Our method significantly outperforms prior loss-based approaches, revealing context-dependent memorization patterns in pre-trained LLMs.<|reference_end|>
|
arxiv
|
@article{chang2024context-aware,
title={Context-Aware Membership Inference Attacks against Pre-trained Large
Language Models},
author={Hongyan Chang, Ali Shahin Shamsabadi, Kleomenis Katevas, Hamed
Haddadi, Reza Shokri},
journal={arXiv preprint arXiv:2409.13745},
year={2024},
archivePrefix={arXiv},
eprint={2409.13745},
primaryClass={cs.CL cs.AI cs.CR cs.LG stat.ML}
}
|
chang2024context-aware
|
arxiv-660069
|
2409.13746
|
When Less Is Not More: Large Language Models Normalize Less-Frequent Terms with Lower Accuracy
|
<|reference_start|>When Less Is Not More: Large Language Models Normalize Less-Frequent Terms with Lower Accuracy: Term normalization is the process of mapping a term from free text to a standardized concept and its machine-readable code in an ontology. Accurate normalization of terms that capture phenotypic differences between patients and diseases is critical to the success of precision medicine initiatives. A large language model (LLM), such as GPT-4o, can normalize terms to the Human Phenotype Ontology (HPO), but it may retrieve incorrect HPO IDs. Reported accuracy rates for LLMs on these tasks may be inflated due to imbalanced test datasets skewed towards high-frequency terms. In our study, using a comprehensive dataset of 268,776 phenotype annotations for 12,655 diseases from the HPO, GPT-4o achieved an accuracy of 13.1% in normalizing 11,225 unique terms. However, the accuracy was unevenly distributed, with higher-frequency and shorter terms normalized more accurately than lower-frequency and longer terms. Feature importance analysis, using SHAP and permutation methods, identified low-term frequency as the most significant predictor of normalization errors. These findings suggest that training and evaluation datasets for LLM-based term normalization should balance low- and high-frequency terms to improve model performance, particularly for infrequent terms critical to precision medicine.<|reference_end|>
|
arxiv
|
@article{hier2024when,
title={When Less Is Not More: Large Language Models Normalize Less-Frequent
Terms with Lower Accuracy},
author={Daniel B. Hier and Thanh Son Do and Tayo Obafemi-Ajayi},
journal={arXiv preprint arXiv:2409.13746},
year={2024},
archivePrefix={arXiv},
eprint={2409.13746},
primaryClass={cs.CL cs.AI}
}
|
hier2024when
|
arxiv-660070
|
2409.13747
|
Machine Translation with Large Language Models: Decoder Only vs Encoder-Decoder
|
<|reference_start|>Machine Translation with Large Language Models: Decoder Only vs Encoder-Decoder: This project, titled "Machine Translation with Large Language Models: Decoder-only vs. Encoder-Decoder," aims to develop a multilingual machine translation (MT) model. Focused on Indian regional languages, especially Telugu, Tamil, and Malayalam, the model seeks to enable accurate and contextually appropriate translations across diverse language pairs. By comparing Decoder-only and Encoder-Decoder architectures, the project aims to optimize translation quality and efficiency, advancing cross-linguistic communication tools.The primary objective is to develop a model capable of delivering high-quality translations that are accurate and contextually appropriate. By leveraging large language models, specifically comparing the effectiveness of Decoder-only and Encoder-Decoder architectures, the project seeks to optimize translation performance and efficiency across multilingual contexts. Through rigorous experimentation and analysis, this project aims to advance the field of machine translation, contributing valuable insights into the effectiveness of different model architectures and paving the way for enhanced cross-linguistic communication tools.<|reference_end|>
|
arxiv
|
@article{m.2024machine,
title={Machine Translation with Large Language Models: Decoder Only vs.
Encoder-Decoder},
author={Abhinav P.M., SujayKumar Reddy M, Oswald Christopher},
journal={arXiv preprint arXiv:2409.13747},
year={2024},
archivePrefix={arXiv},
eprint={2409.13747},
primaryClass={cs.CL cs.ET cs.LG}
}
|
m.2024machine
|
arxiv-660071
|
2409.13748
|
TheraGen: Therapy for Every Generation
|
<|reference_start|>TheraGen: Therapy for Every Generation: We present TheraGen, an advanced AI-powered mental health chatbot utilizing the LLaMA 2 7B model. This approach builds upon recent advancements in language models and transformer architectures. TheraGen provides all-day personalized, compassionate mental health care by leveraging a large dataset of 1 million conversational entries, combining anonymized therapy transcripts, online mental health discussions, and psychological literature, including APA resources. Our implementation employs transfer learning, fine-tuning, and advanced training techniques to optimize performance. TheraGen offers a user-friendly interface for seamless interaction, providing empathetic responses and evidence-based coping strategies. Evaluation results demonstrate high user satisfaction rates, with 94% of users reporting improved mental well-being. The system achieved a BLEU score of 0.67 and a ROUGE score of 0.62, indicating strong response accuracy. With an average response time of 1395 milliseconds, TheraGen ensures real-time, efficient support. While not a replacement for professional therapy, TheraGen serves as a valuable complementary tool, significantly improving user well-being and addressing the accessibility gap in mental health treatments. This paper details TheraGen's architecture, training methodology, ethical considerations, and future directions, contributing to the growing field of AI-assisted mental healthcare and offering a scalable solution to the pressing need for mental health support.<|reference_end|>
|
arxiv
|
@article{doshi2024theragen:,
title={TheraGen: Therapy for Every Generation},
author={Kartikey Doshi, Jimit Shah, Narendra Shekokar},
journal={arXiv preprint arXiv:2409.13748},
year={2024},
archivePrefix={arXiv},
eprint={2409.13748},
primaryClass={cs.CL cs.AI cs.HC}
}
|
doshi2024theragen:
|
arxiv-660072
|
2409.13749
|
KodeXv01: A Family of State-of-the-Art Financial Large Language Models
|
<|reference_start|>KodeXv01: A Family of State-of-the-Art Financial Large Language Models: Although powerful, current cutting-edge LLMs may not fulfil the needs of highly specialised sectors. We introduce KodeXv0.1, a family of large language models that outclass GPT-4 in financial question answering. We utilise the base variants of Llama 3.1 8B and 70B and adapt them to the financial domain through a custom training regime. To this end, we collect and process a large number of publicly available financial documents such as earnings calls and business reports. These are used to generate a high-quality, synthetic dataset consisting of Context-Question-Answer triplets which closely mirror real-world financial tasks. Using the train split of this dataset, we perform RAG-aware 4bit LoRA instruction tuning runs of Llama 3.1 base variants to produce KodeX-8Bv0.1 and KodeX-70Bv0.1. We then complete extensive model evaluations using FinanceBench, FinQABench and the withheld test split of our dataset. Our results show that KodeX-8Bv0.1 is more reliable in financial contexts than cutting-edge instruct models in the same parameter regime, surpassing them by up to 9.24%. In addition, it is even capable of outperforming state-of-the-art proprietary models such as GPT-4 by up to 7.07%. KodeX-70Bv0.1 represents a further improvement upon this, exceeding GPT-4's performance on every tested benchmark.<|reference_end|>
|
arxiv
|
@article{rajani2024kodexv0.1:,
title={KodeXv0.1: A Family of State-of-the-Art Financial Large Language Models},
author={Neel Rajani, Lilli Kiessling, Aleksandr Ogaltsov, Claus Lang},
journal={arXiv preprint arXiv:2409.13749},
year={2024},
archivePrefix={arXiv},
eprint={2409.13749},
primaryClass={cs.CL cs.AI q-fin.CP}
}
|
rajani2024kodexv0.1:
|
arxiv-660073
|
2409.13750
|
Undergrads Are All You Have
|
<|reference_start|>Undergrads Are All You Have: The outsourcing of busy work and other research-related tasks to undergraduate students is a time-honored academic tradition. In recent years, these tasks have been given to Lama-based large-language models such as Alpaca and Llama increasingly often, putting poor undergraduate students out of work. Due to the costs associated with importing and caring for South American Camelidae, researcher James Yoo set out to find a cheaper and more effective alternative to these models. The findings, published in the highly-respected journal, SIGBOVIK, demonstrates that their model, GPT-UGRD is on par with, and in some cases better, than Lama models for natural language processing tasks. The paper also demonstrates that GPT-UGRD is cheaper and easier to train and operate than transformer models. In this paper, we outline the implementation, application, multi-tenanting, and social implications of using this new model in research and other contexts.<|reference_end|>
|
arxiv
|
@article{neth2024undergrads,
title={Undergrads Are All You Have},
author={Ashe Neth},
journal={arXiv preprint arXiv:2409.13750},
year={2024},
archivePrefix={arXiv},
eprint={2409.13750},
primaryClass={cs.CY}
}
|
neth2024undergrads
|
arxiv-660074
|
2409.13752
|
Thinking Before Speaking: A Role-playing Model with Mindset
|
<|reference_start|>Thinking Before Speaking: A Role-playing Model with Mindset: Role-playing is an easy task for Large Language Models (LLMs), as they are skilled at simulating human behaviors. Many current studies have enabled LLMs to generate responses in the tone of a specific role by fine-tuning the models or using specialized prompts. However, it is typically easy to recognize when a role is being played by LLMs. These models tend to perform poorly when confronted with knowledge that the assumed role does not possess, or a question that requires the specific experience or logic of the role to answer. To address this problem and make LLMs act more like real roles, we propose a Thinking Before Speaking (TBS) model in this paper. Unlike other studies, we first extend the data based on the character's real-life scenarios and the historical dialogue, supplementing each pair of dialogue with the character's mindset. Then we add few data points that include elements beyond the role's knowledge, and fine-tune the LLMs. This approach can help LLMs adopt the role's thought process and logic, avoiding responses that fall outside the role's knowledge base. We have also prepared a dataset and evaluation metrics to test these capabilities. Experimental results show that our TBS model can better emulate a role in terms of tone, knowledge, and mindset.<|reference_end|>
|
arxiv
|
@article{zhang2024thinking,
title={Thinking Before Speaking: A Role-playing Model with Mindset},
author={Baohua Zhang, Yongyi Huang, Wenyao Cui, Huaping Zhang},
journal={arXiv preprint arXiv:2409.13752},
year={2024},
archivePrefix={arXiv},
eprint={2409.13752},
primaryClass={cs.CL cs.AI}
}
|
zhang2024thinking
|
arxiv-660075
|
2409.13753
|
Synergistic Simulations: Multi-Agent Problem Solving with Large Language Models
|
<|reference_start|>Synergistic Simulations: Multi-Agent Problem Solving with Large Language Models: Large Language Models (LLMs) have increasingly demonstrated the ability to facilitate the development of multi-agent systems that allow the interpretation of thoughts and actions generated by each individual. Promising advancements have also been made in LLM-based interaction with existing worlds, particularly in interacting with simulated environments. This paper aims to integrate both aforementioned topics (agents & world interaction) into a single simulation where multiple agents can work together to solve a problem, modeling how groups of humans can often solve problems better than individuals. By showing whether LLMs demonstrate the synergy of human collaboration, it could lead to advancements in the applications of LLMs. We implemented two simulations: a physical studio apartment with two roommates, and another where agents collaborate to complete a programming task. We provide a multi-agent framework, discuss the performance of the agents in each simulation, and discuss potential future additions.<|reference_end|>
|
arxiv
|
@article{sprigler2024synergistic,
title={Synergistic Simulations: Multi-Agent Problem Solving with Large Language
Models},
author={Asher Sprigler, Alexander Drobek, Keagan Weinstock, Wendpanga Tapsoba,
Gavin Childress, Andy Dao and Lucas Gral},
journal={arXiv preprint arXiv:2409.13753},
year={2024},
archivePrefix={arXiv},
eprint={2409.13753},
primaryClass={cs.MA cs.AI cs.CL cs.ET}
}
|
sprigler2024synergistic
|
arxiv-660076
|
2409.13754
|
Increasing the Value of Information During Planning in Uncertain Environments
|
<|reference_start|>Increasing the Value of Information During Planning in Uncertain Environments: Prior studies have demonstrated that for many real-world problems, POMDPs can be solved through online algorithms both quickly and with near optimality. However, on an important set of problems where there is a large time delay between when the agent can gather information and when it needs to use that information, these solutions fail to adequately consider the value of information. As a result, information gathering actions, even when they are critical in the optimal policy, will be ignored by existing solutions, leading to sub-optimal decisions by the agent. In this research, we develop a novel solution that rectifies this problem by introducing a new algorithm that improves upon state-of-the-art online planning by better reflecting on the value of actions that gather information. We do this by adding Entropy to the UCB1 heuristic in the POMCP algorithm. We test this solution on the hallway problem. Results indicate that our new algorithm performs significantly better than POMCP.<|reference_end|>
|
arxiv
|
@article{pokharel2024increasing,
title={Increasing the Value of Information During Planning in Uncertain
Environments},
author={Gaurab Pokharel},
journal={arXiv preprint arXiv:2409.13754},
year={2024},
archivePrefix={arXiv},
eprint={2409.13754},
primaryClass={cs.AI cs.MA cs.RO}
}
|
pokharel2024increasing
|
arxiv-660077
|
2409.13755
|
Entity-Aware Self-Attention and Contextualized GCN for Enhanced Relation Extraction in Long Sentences
|
<|reference_start|>Entity-Aware Self-Attention and Contextualized GCN for Enhanced Relation Extraction in Long Sentences: Relation extraction as an important natural Language processing (NLP) task is to identify relations between named entities in text. Recently, graph convolutional networks over dependency trees have been widely used to capture syntactic features and achieved attractive performance. However, most existing dependency-based approaches ignore the positive influence of the words outside the dependency trees, sometimes conveying rich and useful information on relation extraction. In this paper, we propose a novel model, Entity-aware Self-attention Contextualized GCN (ESC-GCN), which efficiently incorporates syntactic structure of input sentences and semantic context of sequences. To be specific, relative position self-attention obtains the overall semantic pairwise correlation related to word position, and contextualized graph convolutional networks capture rich intra-sentence dependencies between words by adequately pruning operations. Furthermore, entity-aware attention layer dynamically selects which token is more decisive to make final relation prediction. In this way, our proposed model not only reduces the noisy impact from dependency trees, but also obtains easily-ignored entity-related semantic representation. Extensive experiments on various tasks demonstrate that our model achieves encouraging performance as compared to existing dependency-based and sequence-based models. Specially, our model excels in extracting relations between entities of long sentences.<|reference_end|>
|
arxiv
|
@article{wang2024entity-aware,
title={Entity-Aware Self-Attention and Contextualized GCN for Enhanced Relation
Extraction in Long Sentences},
author={Xin Wang, Xinyi Bai},
journal={arXiv preprint arXiv:2409.13755},
year={2024},
archivePrefix={arXiv},
eprint={2409.13755},
primaryClass={cs.CL cs.AI}
}
|
wang2024entity-aware
|
arxiv-660078
|
2409.13756
|
Language Models Learn Metadata: Political Stance Detection Case Study
|
<|reference_start|>Language Models Learn Metadata: Political Stance Detection Case Study: Stance detection is a crucial NLP task with numerous applications in social science, from analyzing online discussions to assessing political campaigns. This paper investigates the optimal way to incorporate metadata into a political stance detection task. We demonstrate that previous methods combining metadata with language-based data for political stance detection have not fully utilized the metadata information; our simple baseline, using only party membership information, surpasses the current state-of-the-art. We then show that prepending metadata (e.g., party and policy) to political speeches performs best, outperforming all baselines, indicating that complex metadata inclusion systems may not learn the task optimally.<|reference_end|>
|
arxiv
|
@article{cao2024language,
title={Language Models Learn Metadata: Political Stance Detection Case Study},
author={Stanley Cao, Felix Drinkall},
journal={arXiv preprint arXiv:2409.13756},
year={2024},
archivePrefix={arXiv},
eprint={2409.13756},
primaryClass={cs.CL}
}
|
cao2024language
|
arxiv-660079
|
2409.13757
|
Efficient Hybrid Inference for LLMs: Reward-Based Token Modelling with Selective Cloud Assistance
|
<|reference_start|>Efficient Hybrid Inference for LLMs: Reward-Based Token Modelling with Selective Cloud Assistance: Large language models (LLMs) are known for their exceptional performance across a range of natural language processing tasks, but their deployment comes at a high computational and financial cost. On the other hand, smaller language models (SLMs), which can be deployed on lower-cost edge devices, struggle to match the performance of their larger counterparts. This paper presents a novel hybrid inference approach that leverages the strengths of both model types while minimizing reliance on costly cloud-based LLMs. Unlike existing methods that route entire queries to either an SLM or a cloud LLM, our approach introduces a reward-based mechanism to dynamically determine the involvement of the cloud LLM during token generation. Specifically, each token predicted by the SLM is evaluated against a reward score, and only when this score falls below a certain threshold is the cloud LLM consulted for assistance in the next token prediction. This method not only reduces the traffic to the cloud LLM, thereby lowering costs, but also allows for flexible control over response quality depending on the reward score threshold. Experimental results demonstrate that our approach significantly reduces cloud LLM usage with minimal impact on overall response quality, offering a cost-effective solution for deploying high-performance language models<|reference_end|>
|
arxiv
|
@article{ms2024efficient,
title={Efficient Hybrid Inference for LLMs: Reward-Based Token Modelling with
Selective Cloud Assistance},
author={Adarsh MS, Jithin VG, Ditto PS},
journal={arXiv preprint arXiv:2409.13757},
year={2024},
archivePrefix={arXiv},
eprint={2409.13757},
primaryClass={cs.CL}
}
|
ms2024efficient
|
arxiv-660080
|
2409.13758
|
Optimizing the Songwriting Process: Genre-Based Lyric Generation Using Deep Learning Models
|
<|reference_start|>Optimizing the Songwriting Process: Genre-Based Lyric Generation Using Deep Learning Models: The traditional songwriting process is rather complex and this is evident in the time it takes to produce lyrics that fit the genre and form comprehensive verses. Our project aims to simplify this process with deep learning techniques, thus optimizing the songwriting process and enabling an artist to hit their target audience by staying in genre. Using a dataset of 18,000 songs off Spotify, we developed a unique preprocessing format using tokens to parse lyrics into individual verses. These results were used to train a baseline pretrained seq2seq model, and a LSTM-based neural network models according to song genres. We found that generation yielded higher recall (ROUGE) in the baseline model, but similar precision (BLEU) for both models. Qualitatively, we found that many of the lyrical phrases generated by the original model were still comprehensible and discernible between which genres they fit into, despite not necessarily being the exact the same as the true lyrics. Overall, our results yielded that lyric generation can reasonably be sped up to produce genre-based lyrics and aid in hastening the songwriting process.<|reference_end|>
|
arxiv
|
@article{cai2024optimizing,
title={Optimizing the Songwriting Process: Genre-Based Lyric Generation Using
Deep Learning Models},
author={Tracy Cai, Wilson Liang, Donte Townes},
journal={arXiv preprint arXiv:2409.13758},
year={2024},
archivePrefix={arXiv},
eprint={2409.13758},
primaryClass={cs.CL cs.AI cs.SD eess.AS}
}
|
cai2024optimizing
|
arxiv-660081
|
2409.13759
|
Simulaci\'on de la distribuci\'on de alimento en el cultivo de camar\'on
|
<|reference_start|>Simulaci\'on de la distribuci\'on de alimento en el cultivo de camar\'on: This document presents the experimentation of 4 cases of food distribution for shrimp farming. The distributions are based on the location of the automatic feeders. Three cases applied in reality and a fourth case where the food is irrigated on the crop simultaneously and uniformly. In a first stage, the simulation of the three distribution cases is successfully adjusted to reality, where the trend of the shrimp growth curve is correlated with the historical data curve. A second stage where you experiment in 16 configurations that are based on the amount of food, the density of biomass and the distribution of the food. The simulation adopts the concepts of genetic algorithms to improve the population and fuzzy logic as an agent evaluation technique for decision-making against the quality of physical-chemical parameters in the simulated environment. The results of these interactions reveal a reduction in the simulated total culture time from 22 weeks to 14 weeks.<|reference_end|>
|
arxiv
|
@article{rosado2024simulaci\'on,
title={Simulaci\'on de la distribuci\'on de alimento en el cultivo de camar\'on},
author={Renato L. Conforme Rosado and Francisco C. Calderon Bocanegra},
journal={arXiv preprint arXiv:2409.13759},
year={2024},
archivePrefix={arXiv},
eprint={2409.13759},
primaryClass={cs.AI}
}
|
rosado2024simulaci\'on
|
arxiv-660082
|
2409.13761
|
Do Large Language Models Need a Content Delivery Network?
|
<|reference_start|>Do Large Language Models Need a Content Delivery Network?: As the use of large language models (LLMs) expands rapidly, so does the range of knowledge needed to supplement various LLM queries. Thus, enabling flexible and efficient injection of new knowledge in LLM inference is critical. Three high-level options exist: (i) embedding the knowledge in LLM's weights (i.e., fine-tuning), (ii) including the knowledge as a part of LLM's text input (i.e., in-context learning), or (iii) injecting the KV caches of the new knowledge to LLM during prefill. This paper argues that, although fine-tuning and in-context learning are popular, using KV caches as the medium of knowledge could simultaneously enable more modular management of knowledge injection and more efficient LLM serving with low cost and fast response. To realize these benefits, we envision a Knowledge Delivery Network (KDN), a new system component in LLM services that dynamically optimizes the storage, transfer, and composition of KV cache across LLM engines and other compute and storage resources. We believe that, just like content delivery networks (CDNs), such as Akamai, enabled the success of the Internet ecosystem through their efficient data delivery, KDNs will be critical to the success of LLM applications through their efficient knowledge delivery. We have open-sourced a KDN prototype at https://github.com/LMCache/LMCache.<|reference_end|>
|
arxiv
|
@article{cheng2024do,
title={Do Large Language Models Need a Content Delivery Network?},
author={Yihua Cheng, Kuntai Du, Jiayi Yao, Junchen Jiang},
journal={arXiv preprint arXiv:2409.13761},
year={2024},
archivePrefix={arXiv},
eprint={2409.13761},
primaryClass={cs.CL cs.AI}
}
|
cheng2024do
|
arxiv-660083
|
2409.13764
|
Local Explanations and Self-Explanations for Assessing Faithfulness in black-box LLMs
|
<|reference_start|>Local Explanations and Self-Explanations for Assessing Faithfulness in black-box LLMs: This paper introduces a novel task to assess the faithfulness of large language models (LLMs) using local perturbations and self-explanations. Many LLMs often require additional context to answer certain questions correctly. For this purpose, we propose a new efficient alternative explainability technique, inspired by the commonly used leave-one-out approach. Using this approach, we identify the sufficient and necessary parts for the LLM to generate correct answers, serving as explanations. We propose a metric for assessing faithfulness that compares these crucial parts with the self-explanations of the model. Using the Natural Questions dataset, we validate our approach, demonstrating its effectiveness in explaining model decisions and assessing faithfulness.<|reference_end|>
|
arxiv
|
@article{fragkathoulas2024local,
title={Local Explanations and Self-Explanations for Assessing Faithfulness in
black-box LLMs},
author={Christos Fragkathoulas, Odysseas S. Chlapanis},
journal={arXiv preprint arXiv:2409.13764},
year={2024},
doi={10.1145/3688671.3688775},
archivePrefix={arXiv},
eprint={2409.13764},
primaryClass={cs.CL cs.AI}
}
|
fragkathoulas2024local
|
arxiv-660084
|
2409.13765
|
A microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception
|
<|reference_start|>A microscopic investigation of the effect of random envelope fluctuations on phoneme-in-noise perception: In this study, we investigated the effect of specific noise realizations on the discrimination of two consonants, /b/ and /d/. For this purpose, we collected data from twelve participants, who listened to the words /aba/ or /ada/ embedded in one of three background noises. All noises had the same long-term spectrum but differed in the amount of random envelope fluctuations. The data were analyzed on a trial-by-trial basis using the reverse-correlation method. The results revealed that it is possible to predict the categorical responses with better-than-chance accuracy purely based on the spectro-temporal distribution of the random envelope fluctuations of the corresponding noises, without taking into account the actual targets or the signal-to-noise ratios used in the trials. The effect of the noise fluctuations explained on average 8.1% of the participants' responses in white noise, a proportion that increased up to 13.3% for noises with a larger amount of fluctuations. The estimated time-frequency weights revealed that the measured effect originated from confusions between noise fluctuations and relevant acoustic cues from the target words. Substantially similar conclusions were obtained from simulations using an artificial listener. We argue that this token-specific effect of noise is a form of informational masking.<|reference_end|>
|
arxiv
|
@article{osses2024a,
title={A microscopic investigation of the effect of random envelope
fluctuations on phoneme-in-noise perception},
author={Alejandro Osses (LSP, DEC, ENS-PSL), L'eo Varnet (LSP)},
journal={Journal of the Acoustical Society of America, 2024, 155 (2),
pp.1469-1485},
year={2024},
doi={10.1101/2022.12.27.522040},
archivePrefix={arXiv},
eprint={2409.13765},
primaryClass={cs.SD eess.AS}
}
|
osses2024a
|
arxiv-660085
|
2409.13768
|
Magika: AI-Powered Content-Type Detection
|
<|reference_start|>Magika: AI-Powered Content-Type Detection: The task of content-type detection -- which entails identifying the data encoded in an arbitrary byte sequence -- is critical for operating systems, development, reverse engineering environments, and a variety of security applications. In this paper, we introduce Magika, a novel AI-powered content-type detection tool. Under the hood, Magika employs a deep learning model that can execute on a single CPU with just 1MB of memory to store the model's weights. We show that Magika achieves an average F1 score of 99% across over a hundred content types and a test set of more than 1M files, outperforming all existing content-type detection tools today. In order to foster adoption and improvements, we open source Magika under an Apache 2 license on GitHub and make our model and training pipeline publicly available. Our tool has already seen adoption by the Gmail email provider for attachment scanning, and it has been integrated with VirusTotal to aid with malware analysis. We note that this paper discusses the first iteration of Magika, and a more recent version already supports more than 200 content types. The interested reader can see the latest development on the Magika GitHub repository, available at https://github.com/google/magika.<|reference_end|>
|
arxiv
|
@article{fratantonio2024magika:,
title={Magika: AI-Powered Content-Type Detection},
author={Yanick Fratantonio, Luca Invernizzi, Loua Farah, Kurt Thomas, Marina
Zhang, Ange Albertini, Francois Galilee, Giancarlo Metitieri, Julien Cretin,
Alex Petit-Bianco, David Tao, Elie Bursztein},
journal={arXiv preprint arXiv:2409.13768},
year={2024},
archivePrefix={arXiv},
eprint={2409.13768},
primaryClass={cs.CR cs.AI}
}
|
fratantonio2024magika:
|
arxiv-660086
|
2409.13770
|
A constrained optimization approach to improve robustness of neural networks
|
<|reference_start|>A constrained optimization approach to improve robustness of neural networks: In this paper, we present a novel nonlinear programming-based approach to fine-tune pre-trained neural networks to improve robustness against adversarial attacks while maintaining high accuracy on clean data. Our method introduces adversary-correction constraints to ensure correct classification of adversarial data and minimizes changes to the model parameters. We propose an efficient cutting-plane-based algorithm to iteratively solve the large-scale nonconvex optimization problem by approximating the feasible region through polyhedral cuts and balancing between robustness and accuracy. Computational experiments on standard datasets such as MNIST and CIFAR10 demonstrate that the proposed approach significantly improves robustness, even with a very small set of adversarial data, while maintaining minimal impact on accuracy.<|reference_end|>
|
arxiv
|
@article{zhao2024a,
title={A constrained optimization approach to improve robustness of neural
networks},
author={Shudian Zhao, Jan Kronqvist},
journal={arXiv preprint arXiv:2409.13770},
year={2024},
archivePrefix={arXiv},
eprint={2409.13770},
primaryClass={cs.LG math.OC}
}
|
zhao2024a
|
arxiv-660087
|
2409.13773
|
A Case Study of Web App Coding with OpenAI Reasoning Models
|
<|reference_start|>A Case Study of Web App Coding with OpenAI Reasoning Models: This paper presents a case study of coding tasks by the latest reasoning models of OpenAI, i.e. o1-preview and o1-mini, in comparison with other frontier models. The o1 models deliver SOTA results for WebApp1K, a single-task benchmark. To this end, we introduce WebApp1K-Duo, a harder benchmark doubling number of tasks and test cases. The new benchmark causes the o1 model performances to decline significantly, falling behind Claude 3.5. Moreover, they consistently fail when confronted with atypical yet correct test cases, a trap non-reasoning models occasionally avoid. We hypothesize that the performance variability is due to instruction comprehension. Specifically, the reasoning mechanism boosts performance when all expectations are captured, meanwhile exacerbates errors when key expectations are missed, potentially impacted by input lengths. As such, we argue that the coding success of reasoning models hinges on the top-notch base model and SFT to ensure meticulous adherence to instructions.<|reference_end|>
|
arxiv
|
@article{cui2024a,
title={A Case Study of Web App Coding with OpenAI Reasoning Models},
author={Yi Cui},
journal={arXiv preprint arXiv:2409.13773},
year={2024},
archivePrefix={arXiv},
eprint={2409.13773},
primaryClass={cs.SE cs.AI cs.CL}
}
|
cui2024a
|
arxiv-660088
|
2409.13774
|
Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space
|
<|reference_start|>Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space: This work introduces a novel method for enhancing confidence in anomaly detection in Intrusion Detection Systems (IDS) through the use of a Variational Autoencoder (VAE) architecture. By developing a confidence metric derived from latent space representations, we aim to improve the reliability of IDS predictions against cyberattacks. Applied to the NSL-KDD dataset, our approach focuses on binary classification tasks to effectively distinguish between normal and malicious network activities. The methodology demonstrates a significant enhancement in anomaly detection, evidenced by a notable correlation of 0.45 between the reconstruction error and the proposed metric. Our findings highlight the potential of employing VAEs for more accurate and trustworthy anomaly detection in network security.<|reference_end|>
|
arxiv
|
@article{pitsiorlas2024trustworthy,
title={Trustworthy Intrusion Detection: Confidence Estimation Using Latent
Space},
author={Ioannis Pitsiorlas, George Arvanitakis, Marios Kountouris},
journal={arXiv preprint arXiv:2409.13774},
year={2024},
archivePrefix={arXiv},
eprint={2409.13774},
primaryClass={cs.CR cs.AI cs.LG}
}
|
pitsiorlas2024trustworthy
|
arxiv-660089
|
2409.13779
|
AutoPET III Challenge: Tumor Lesion Segmentation using ResEnc-Model Ensemble
|
<|reference_start|>AutoPET III Challenge: Tumor Lesion Segmentation using ResEnc-Model Ensemble: Positron Emission Tomography (PET) /Computed Tomography (CT) is crucial for diagnosing, managing, and planning treatment for various cancers. Developing reliable deep learning models for the segmentation of tumor lesions in PET/CT scans in a multi-tracer multicenter environment, is a critical area of research. Different tracers, such as Fluorodeoxyglucose (FDG) and Prostate-Specific Membrane Antigen (PSMA), have distinct physiological uptake patterns and data from different centers often vary in terms of acquisition protocols, scanner types, and patient populations. Because of this variability, it becomes more difficult to design reliable segmentation algorithms and generalization techniques due to variations in image quality and lesion detectability. To address this challenge, We trained a 3D Residual encoder U-Net within the no new U-Net framework, aiming to generalize the performance of automatic lesion segmentation of whole body PET/CT scans, across different tracers and clinical sites. Further, We explored several preprocessing techniques and ultimately settled on using the Total Segmentator to crop our training data. Additionally, we applied resampling during this process. During inference, we leveraged test-time augmentations and other post-processing techniques to enhance tumor lesion segmentation. Our team currently hold the top position in the Auto-PET III challenge and outperformed the challenge baseline model in the preliminary test set with Dice score of 0.9627.<|reference_end|>
|
arxiv
|
@article{chutani2024autopet,
title={AutoPET III Challenge: Tumor Lesion Segmentation using ResEnc-Model
Ensemble},
author={Tanya Chutani, Saikiran Bonthu, Pranab Samanta, Nitin Singhal},
journal={arXiv preprint arXiv:2409.13779},
year={2024},
archivePrefix={arXiv},
eprint={2409.13779},
primaryClass={eess.IV cs.AI cs.CV}
}
|
chutani2024autopet
|
arxiv-660090
|
2409.13781
|
Solving Combinatorial Optimization Problems on a Photonic Quantum Computer
|
<|reference_start|>Solving Combinatorial Optimization Problems on a Photonic Quantum Computer: Combinatorial optimization problems pose significant computational challenges across various fields, from logistics to cryptography. Traditional computational methods often struggle with their exponential complexity, motivating exploration into alternative paradigms such as quantum computing. In this paper, we investigate the application of photonic quantum computing to solve combinatorial optimization problems. Leveraging the principles of quantum mechanics, we demonstrate how photonic quantum computers can efficiently explore solution spaces and identify optimal solutions for a range of combinatorial problems. We provide an overview of quantum algorithms tailored for combinatorial optimization for different quantum architectures (boson sampling, quantum annealing and gate-based quantum computing). Additionally, we discuss the advantages and challenges of implementing those algorithms on photonic quantum hardware. Through experiments run on an 8-qumode photonic quantum device, as well as numerical simulations, we evaluate the performance of photonic quantum computers in solving representative combinatorial optimization problems, such as the Max-Cut problem and the Job Shop Scheduling Problem.<|reference_end|>
|
arxiv
|
@article{slysz2024solving,
title={Solving Combinatorial Optimization Problems on a Photonic Quantum
Computer},
author={Mateusz Slysz and Krzysztof Kurowski and Grzegorz Walig'ora},
journal={arXiv preprint arXiv:2409.13781},
year={2024},
archivePrefix={arXiv},
eprint={2409.13781},
primaryClass={quant-ph cs.PF}
}
|
slysz2024solving
|
arxiv-660091
|
2409.13782
|
Aircraft Conflict Detection and Avoidance through Interacting Multiple Model (IMM) Estimation
|
<|reference_start|>Aircraft Conflict Detection and Avoidance through Interacting Multiple Model (IMM) Estimation: The practical problem of tracking a maneuvering aircraft during flight has always been a crucial task in order to safeguard airborne assets from unknown threats. Therefore, the need for an efficient target detection and identification technique is substantial and growing. The multiple model (MM) estimation have proven to be one of the most reliable and accurate among various filtering algorithms. In this paper we will implement the Interacting Multiple Model (IMM) estimation technique for the aforementioned purpose of target identification. This target's motion, though defined by predefined dynamics, is obscured due to the noises from tracking sensors. The algorithm intends to predict the location of target and provide feedback maneuver to the reference aircraft in order to avoid a conflict.<|reference_end|>
|
arxiv
|
@article{manish2024aircraft,
title={Aircraft Conflict Detection and Avoidance through Interacting Multiple
Model (IMM) Estimation},
author={Raja Manish and David Webster},
journal={arXiv preprint arXiv:2409.13782},
year={2024},
archivePrefix={arXiv},
eprint={2409.13782},
primaryClass={eess.SY cs.SY stat.AP}
}
|
manish2024aircraft
|
arxiv-660092
|
2409.13783
|
A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles
|
<|reference_start|>A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative Decision Making of Connected and Automated Vehicles: To solve the problem of lateral and logitudinal joint decision-making of multi-vehicle cooperative driving for connected and automated vehicles (CAVs), this paper proposes a Monte Carlo tree search (MCTS) method with parallel update for multi-agent Markov game with limited horizon and time discounted setting. By analyzing the parallel actions in the multi-vehicle joint action space in the partial-steady-state traffic flow, the parallel update method can quickly exclude potential dangerous actions, thereby increasing the search depth without sacrificing the search breadth. The proposed method is tested in a large number of randomly generated traffic flow. The experiment results show that the algorithm has good robustness and better performance than the SOTA reinforcement learning algorithms and heuristic methods. The vehicle driving strategy using the proposed algorithm shows rationality beyond human drivers, and has advantages in traffic efficiency and safety in the coordinating zone.<|reference_end|>
|
arxiv
|
@article{han2024a,
title={A Value Based Parallel Update MCTS Method for Multi-Agent Cooperative
Decision Making of Connected and Automated Vehicles},
author={Ye Han, Lijun Zhang, Dejian Meng, Xingyu Hu, and Songyu Weng},
journal={arXiv preprint arXiv:2409.13783},
year={2024},
archivePrefix={arXiv},
eprint={2409.13783},
primaryClass={cs.MA cs.AI cs.GT cs.SY eess.SY}
}
|
han2024a
|
arxiv-660093
|
2409.13786
|
Physics-informed kernel learning
|
<|reference_start|>Physics-informed kernel learning: Physics-informed machine learning typically integrates physical priors into the learning process by minimizing a loss function that includes both a data-driven term and a partial differential equation (PDE) regularization. Building on the formulation of the problem as a kernel regression task, we use Fourier methods to approximate the associated kernel, and propose a tractable estimator that minimizes the physics-informed risk function. We refer to this approach as physics-informed kernel learning (PIKL). This framework provides theoretical guarantees, enabling the quantification of the physical prior's impact on convergence speed. We demonstrate the numerical performance of the PIKL estimator through simulations, both in the context of hybrid modeling and in solving PDEs. In particular, we show that PIKL can outperform physics-informed neural networks in terms of both accuracy and computation time. Additionally, we identify cases where PIKL surpasses traditional PDE solvers, particularly in scenarios with noisy boundary conditions.<|reference_end|>
|
arxiv
|
@article{doumèche2024physics-informed,
title={Physics-informed kernel learning},
author={Nathan Doum`eche (LPSM, EDF R&D OSIRIS), Francis Bach (PSL), G'erard
Biau (SU, IUF), Claire Boyer (IUF)},
journal={arXiv preprint arXiv:2409.13786},
year={2024},
archivePrefix={arXiv},
eprint={2409.13786},
primaryClass={stat.ML cs.LG math.ST stat.TH}
}
|
doumèche2024physics-informed
|
arxiv-660094
|
2409.13787
|
Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification
|
<|reference_start|>Learning to Generalize Unseen Domains via Multi-Source Meta Learning for Text Classification: With the rapid development of deep learning methods, there have been many breakthroughs in the field of text classification. Models developed for this task have been shown to achieve high accuracy. However, most of these models are trained using labeled data from seen domains. It is difficult for these models to maintain high accuracy in a new challenging unseen domain, which is directly related to the generalization of the model. In this paper, we study the multi-source Domain Generalization of text classification and propose a framework to use multiple seen domains to train a model that can achieve high accuracy in an unseen domain. Specifically, we propose a multi-source meta-learning Domain Generalization framework to simulate the process of model generalization to an unseen domain, so as to extract sufficient domain-related features. We introduced a memory mechanism to store domain-specific features, which coordinate with the meta-learning framework. Besides, we adopt the novel "jury" mechanism that enables the model to learn sufficient domain-invariant features. Experiments demonstrate that our meta-learning framework can effectively enhance the ability of the model to generalize to an unseen domain and can outperform the state-of-the-art methods on multi-source text classification datasets.<|reference_end|>
|
arxiv
|
@article{hu2024learning,
title={Learning to Generalize Unseen Domains via Multi-Source Meta Learning for
Text Classification},
author={Yuxuan Hu, Chenwei Zhang, Min Yang, Xiaodan Liang, Chengming Li, and
Xiping Hu},
journal={arXiv preprint arXiv:2409.13787},
year={2024},
archivePrefix={arXiv},
eprint={2409.13787},
primaryClass={cs.LG cs.AI cs.CL}
}
|
hu2024learning
|
arxiv-660095
|
2409.13788
|
Quantum evolutionary algorithm for TSP combinatorial optimisation problem
|
<|reference_start|>Quantum evolutionary algorithm for TSP combinatorial optimisation problem: This paper implements a new way of solving a problem called the traveling salesman problem (TSP) using quantum genetic algorithm (QGA). We compared how well this new approach works to the traditional method known as a classical genetic algorithm (CGA). The TSP is a well-established challenge in combinatorial optimization where the objective is to find the most efficient path to visit a series of cities, minimizing the total distance, and returning to the starting point. We chose the TSP to test the performance of both algorithms because of its computational complexity and importance in practical applications. We choose the dataset from the international standard library TSPLIB for our experiments. By designing and implementing both algorithms and conducting experiments on various sizes and types of TSP instances, we provide an in-depth analysis of the accuracy of the optimal solution, the number of iterations, the execution time, and the stability of the algorithms for both. The empirical findings indicate that the CGA outperforms the QGA in terms of finding superior solutions more quickly in most of the test instances, especially when the problem size is large. This suggests that although the principle of quantum computing provides a new way to solve complex combinatorial optimisation problems, the implementation of quantum phenomena and the setting of parameters such as the optimal angle for a quantum revolving gate is challenging and need further optimisation to achieve the desired results. Additionally, it is important to note that the QGA has not been tested on real quantum hardware, so its true performance remains unverified. These limitations provide rich opportunities for further research in the future.<|reference_end|>
|
arxiv
|
@article{ma2024quantum,
title={Quantum evolutionary algorithm for TSP combinatorial optimisation
problem},
author={Yijiang Ma, Tan Chye Cheah},
journal={arXiv preprint arXiv:2409.13788},
year={2024},
archivePrefix={arXiv},
eprint={2409.13788},
primaryClass={quant-ph cs.NE}
}
|
ma2024quantum
|
arxiv-660096
|
2409.13789
|
Reduced bit median quantization: A middle process for Efficient Image Compression
|
<|reference_start|>Reduced bit median quantization: A middle process for Efficient Image Compression: Image compression techniques have made remarkable progress when it comes to file size reduction with a tolerable quality reduction; nonetheless, they are facing some challenges when it comes to applying more compression with the same perceptible quality or in accounting for specific use cases such as deep archive files and more efficient image transfers. Previous techniques have tried to solve the former problem by applying one specific or a combination of different algorithms. However, none of these methods were able to achieve additional file size reduction beyond a certain compression. We introduce Reduced Bit Median Quantization (RBMQ), a middle-process image compression technique designed to enhance file size reduction so that it can be stored with already existing file extension formats.in RBMQ by applying only the first step in which the quantization of valued further file size reduction can be achieved without a noticeable decrease in the image quality. Furthermore, more size reduction can be achieved by reducing the representing bits for the quantized values which can be optimal for deep archival storage or big-size image transfer in which the image quality is not suitable for the human eye since it is dark and dim but can be much efficient to interact with network and storage components later to be decoded to get the only quantized value image that almost the same quality with the original one. RBMQ introduces redundancy to the pixel values to be taken advantage of by existing compression techniques furthermore it introduces bit reduction from 8 to 5 bits for image file extensions such as jpeg which substantially reduces the file size to be used for JPEG file transfers and deep archive storage.<|reference_end|>
|
arxiv
|
@article{abebayew2024reduced,
title={Reduced bit median quantization: A middle process for Efficient Image
Compression},
author={Fikresilase Wondmeneh Abebayew},
journal={arXiv preprint arXiv:2409.13789},
year={2024},
archivePrefix={arXiv},
eprint={2409.13789},
primaryClass={eess.IV cs.IT math.IT}
}
|
abebayew2024reduced
|
arxiv-660097
|
2409.13790
|
Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasaurus
|
<|reference_start|>Revisiting Synthetic Human Trajectories: Imitative Generation and Benchmarks Beyond Datasaurus: Human trajectory data, which plays a crucial role in various applications such as crowd management and epidemic prevention, is challenging to obtain due to practical constraints and privacy concerns. In this context, synthetic human trajectory data is generated to simulate as close as possible to real-world human trajectories, often under summary statistics and distributional similarities. However, the complexity of human mobility patterns is oversimplified by these similarities (a.k.a. ``Datasaurus''), resulting in intrinsic biases in both generative model design and benchmarks of the generated trajectories. Against this background, we propose MIRAGE, a huMan-Imitative tRAjectory GenErative model designed as a neural Temporal Point Process integrating an Exploration and Preferential Return model. It imitates the human decision-making process in trajectory generation, rather than fitting any specific statistical distributions as traditional methods do, thus avoiding the Datasaurus issue. Moreover, we also propose a comprehensive task-based evaluation protocol beyond Datasaurus to systematically benchmark trajectory generative models on four typical downstream tasks, integrating multiple techniques and evaluation metrics for each task, to comprehensively assess the ultimate utility of the generated trajectories. We conduct a thorough evaluation of MIRAGE on three real-world user trajectory datasets against a sizeable collection of baselines. Results show that compared to the best baselines, MIRAGE-generated trajectory data not only achieves the best statistical and distributional similarities with 59.0-71.5% improvement, but also yields the best performance in the task-based evaluation with 10.9-33.4% improvement.<|reference_end|>
|
arxiv
|
@article{deng2024revisiting,
title={Revisiting Synthetic Human Trajectories: Imitative Generation and
Benchmarks Beyond Datasaurus},
author={Bangchao Deng, Xin Jing, Tianyue Yang, Bingqing Qu, Philippe
Cudre-Mauroux and Dingqi Yang},
journal={arXiv preprint arXiv:2409.13790},
year={2024},
archivePrefix={arXiv},
eprint={2409.13790},
primaryClass={cs.LG cs.AI}
}
|
deng2024revisiting
|
arxiv-660098
|
2409.13791
|
Multi-omics data integration for early diagnosis of hepatocellular carcinoma (HCC) using machine learning
|
<|reference_start|>Multi-omics data integration for early diagnosis of hepatocellular carcinoma (HCC) using machine learning: The complementary information found in different modalities of patient data can aid in more accurate modelling of a patient's disease state and a better understanding of the underlying biological processes of a disease. However, the analysis of multi-modal, multi-omics data presents many challenges, including high dimensionality and varying size, statistical distribution, scale and signal strength between modalities. In this work we compare the performance of a variety of ensemble machine learning algorithms that are capable of late integration of multi-class data from different modalities. The ensemble methods and their variations tested were i) a voting ensemble, with hard and soft vote, ii) a meta learner, iii) a multi-modal Adaboost model using a hard vote, a soft vote and a meta learner to integrate the modalities on each boosting round, the PB-MVBoost model and a novel application of a mixture of experts model. These were compared to simple concatenation as a baseline. We examine these methods using data from an in-house study on hepatocellular carcinoma (HCC), along with four validation datasets on studies from breast cancer and irritable bowel disease (IBD). Using the area under the receiver operating curve as a measure of performance we develop models that achieve a performance value of up to 0.85 and find that two boosted methods, PB-MVBoost and Adaboost with a soft vote were the overall best performing models. We also examine the stability of features selected, and the size of the clinical signature determined. Finally, we provide recommendations for the integration of multi-modal multi-class data.<|reference_end|>
|
arxiv
|
@article{spooner2024multi-omics,
title={Multi-omics data integration for early diagnosis of hepatocellular
carcinoma (HCC) using machine learning},
author={Annette Spooner, Mohammad Karimi Moridani, Azadeh Safarchi, Salim
Maher, Fatemeh Vafaee, Amany Zekry, Arcot Sowmya},
journal={arXiv preprint arXiv:2409.13791},
year={2024},
archivePrefix={arXiv},
eprint={2409.13791},
primaryClass={cs.LG cs.AI}
}
|
spooner2024multi-omics
|
arxiv-660099
|
2409.13792
|
Continual Learning for Multimodal Data Fusion of a Soft Gripper
|
<|reference_start|>Continual Learning for Multimodal Data Fusion of a Soft Gripper: Continual learning (CL) refers to the ability of an algorithm to continuously and incrementally acquire new knowledge from its environment while retaining previously learned information. A model trained on one data modality often fails when tested with a different modality. A straightforward approach might be to fuse the two modalities by concatenating their features and training the model on the fused data. However, this requires retraining the model from scratch each time it encounters a new domain. In this paper, we introduce a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class-incremental and domain-incremental learning scenarios in an artificial environment where labeled data is scarce, yet non-iid (independent and identical distribution) unlabeled data from the environment is plentiful. The proposed algorithm is efficient and only requires storing prototypes for each class. We evaluate the algorithm's effectiveness on a challenging custom multimodal dataset comprising of tactile data from a soft pneumatic gripper, and visual data from non-stationary images of objects extracted from video sequences. Additionally, we conduct an ablation study on the custom dataset and the Core50 dataset to highlight the contributions of different components of the algorithm. To further demonstrate the robustness of the algorithm, we perform a real-time experiment for object classification using the soft gripper and an external independent camera setup, all synchronized with the Robot Operating System (ROS) framework.<|reference_end|>
|
arxiv
|
@article{kushawaha2024continual,
title={Continual Learning for Multimodal Data Fusion of a Soft Gripper},
author={Nilay Kushawaha, Egidio Falotico},
journal={arXiv preprint arXiv:2409.13792},
year={2024},
archivePrefix={arXiv},
eprint={2409.13792},
primaryClass={cs.LG cs.AI cs.RO}
}
|
kushawaha2024continual
|
arxiv-660100
|
2409.13793
|
On the Feasibility of Fully AI-automated Vishing Attacks
|
<|reference_start|>On the Feasibility of Fully AI-automated Vishing Attacks: A vishing attack is a form of social engineering where attackers use phone calls to deceive individuals into disclosing sensitive information, such as personal data, financial information, or security credentials. Attackers exploit the perceived urgency and authenticity of voice communication to manipulate victims, often posing as legitimate entities like banks or tech support. Vishing is a particularly serious threat as it bypasses security controls designed to protect information. In this work, we study the potential for vishing attacks to escalate with the advent of AI. In theory, AI-powered software bots may have the ability to automate these attacks by initiating conversations with potential victims via phone calls and deceiving them into disclosing sensitive information. To validate this thesis, we introduce ViKing, an AI-powered vishing system developed using publicly available AI technology. It relies on a Large Language Model (LLM) as its core cognitive processor to steer conversations with victims, complemented by a pipeline of speech-to-text and text-to-speech modules that facilitate audio-text conversion in phone calls. Through a controlled social experiment involving 240 participants, we discovered that ViKing has successfully persuaded many participants to reveal sensitive information, even those who had been explicitly warned about the risk of vishing campaigns. Interactions with ViKing's bots were generally considered realistic. From these findings, we conclude that tools like ViKing may already be accessible to potential malicious actors, while also serving as an invaluable resource for cyber awareness programs.<|reference_end|>
|
arxiv
|
@article{figueiredo2024on,
title={On the Feasibility of Fully AI-automated Vishing Attacks},
author={Jo~ao Figueiredo, Afonso Carvalho, Daniel Castro, Daniel
Gonc{c}alves, Nuno Santos},
journal={arXiv preprint arXiv:2409.13793},
year={2024},
archivePrefix={arXiv},
eprint={2409.13793},
primaryClass={cs.CR cs.AI eess.AS}
}
|
figueiredo2024on
|
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