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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 21 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 82 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 145 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
Collections
Discover the best community collections!
Collections including paper arxiv:2403.13044
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How Far Are We from Intelligent Visual Deductive Reasoning?
Paper • 2403.04732 • Published • 19 -
MoAI: Mixture of All Intelligence for Large Language and Vision Models
Paper • 2403.07508 • Published • 74 -
DragAnything: Motion Control for Anything using Entity Representation
Paper • 2403.07420 • Published • 13 -
Learning and Leveraging World Models in Visual Representation Learning
Paper • 2403.00504 • Published • 31
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Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis
Paper • 2401.09048 • Published • 9 -
Improving fine-grained understanding in image-text pre-training
Paper • 2401.09865 • Published • 16 -
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Paper • 2401.10891 • Published • 60 -
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
Paper • 2401.13627 • Published • 73
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A Picture is Worth a Thousand Words: Principled Recaptioning Improves Image Generation
Paper • 2310.16656 • Published • 40 -
Unsupervised Universal Image Segmentation
Paper • 2312.17243 • Published • 19 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 113 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 30