[FEEDBACK] Daily Papers
Note that this is not a post about adding new papers, it's about feedback on the Daily Papers community update feature.
How to submit a paper to the Daily Papers, like @akhaliq (AK)?
- Submitting is available to paper authors
- Only recent papers (less than 7d) can be featured on the Daily
Then drop the arxiv id in the form at https://huggingface.co/papers/submit
- Add medias to the paper (images, videos) when relevant
- You can start the discussion to engage with the community
Please check out the documentation
We are excited to share our recent work on MLLM architecture design titled "Ovis: Structural Embedding Alignment for Multimodal Large Language Model".
Paper: https://arxiv.org/abs/2405.20797
Github: https://github.com/AIDC-AI/Ovis
Model: https://huggingface.co/AIDC-AI/Ovis-Clip-Llama3-8B
Data: https://huggingface.co/datasets/AIDC-AI/Ovis-dataset
@Yiwen-ntu for now we support only videos as paper covers in the Daily.
we are excited to share our work titled "Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models" : https://arxiv.org/abs/2406.12644
Hello, everyone. We are pleased to present our paper: "Accelerating Auto-regressive Text-to-Image Generation with Training-free Speculative Jacobi Decoding"
To the best of our knowledge, this is the first training-free acceleration method for auto-regressive text-to-image generation models.
You can access the full paper here: https://arxiv.org/abs/2410.01699
We're thrilled to share our recent works,
- ''Collaborative Performance Prediction for Large Language Models'', While scaling laws have been a popular method for predicting LLM performance on downstream tasks, our research shows that simpler approaches like matrix factorization and neural collaborative filtering can yield even better results. We encourage a collaborative framework where model design information is shared, allowing for accurate predictions of future models' performance on downstream tasks. Our framework supports integration with open-source leaderboards, such as Open Leaderboard and HELM, enabling developers to predict their models' performance by leveraging historical model data. You can access the full paper here: https://arxiv.org/abs/2407.01300.
- ''RevisEval: Improving LLM-as-a-Judge via Response-Adapted References'', Evaluation has long been a cornerstone of progress in text generation capabilities. With the limitations of traditional metrics, LLM-as-a-Judge has become a viable method for assessing generative abilities in open-ended tasks, though it still faces significant reliability gaps compared to human evaluation. By harnessing the revision capabilities of LLMs, we unlock the potential of references in traditional evaluations, generating response-adapted references that can significantly enhance general evaluation methods on various tasks. This approach not only boosts the accuracy of LLM-as-a-Judge but also revives traditional metrics like BLEU, enabling them to effectively evaluate tasks on benchmarks such as MTBench and Alpacafarm, with results that are even comparable to those of LLM-as-a-Judge. It also performs well in using weak LLMs for evaluation and mitigating positional bias. You can access the full paper here: https://arxiv.org/abs/2410.05193
M3GPT: An Advanced Multimodal, Multitask Framework for Motion Comprehension and Generation，https://arxiv.org/pdf/2405.16273 ， accepted by NeurIPS 2024
I don't have a paper, but I made a small sample framework researchers could use for sampling experiments.
