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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
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:2504.05299
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 29 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 14 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23
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FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language
Paper • 2506.20920 • Published • 75 -
SmolVLM: Redefining small and efficient multimodal models
Paper • 2504.05299 • Published • 200 -
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Paper • 2303.03915 • Published • 7 -
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper • 2502.02737 • Published • 250
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FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language
Paper • 2506.20920 • Published • 75 -
SmolVLM: Redefining small and efficient multimodal models
Paper • 2504.05299 • Published • 200 -
YourBench: Easy Custom Evaluation Sets for Everyone
Paper • 2504.01833 • Published • 22 -
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper • 2502.02737 • Published • 250
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The Leaderboard Illusion
Paper • 2504.20879 • Published • 72 -
SmolVLM: Redefining small and efficient multimodal models
Paper • 2504.05299 • Published • 200 -
Seedance 1.0: Exploring the Boundaries of Video Generation Models
Paper • 2506.09113 • Published • 102 -
Small Language Models are the Future of Agentic AI
Paper • 2506.02153 • Published • 21
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VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks
Paper • 2504.05118 • Published • 26 -
T1: Tool-integrated Self-verification for Test-time Compute Scaling in Small Language Models
Paper • 2504.04718 • Published • 42 -
SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement
Paper • 2504.03561 • Published • 18 -
Concept Lancet: Image Editing with Compositional Representation Transplant
Paper • 2504.02828 • Published • 16
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InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
Paper • 2508.18265 • Published • 207 -
SmolVLM: Redefining small and efficient multimodal models
Paper • 2504.05299 • Published • 200 -
Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
Paper • 2504.15271 • Published • 67
-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
-
VAPO: Efficient and Reliable Reinforcement Learning for Advanced Reasoning Tasks
Paper • 2504.05118 • Published • 26 -
T1: Tool-integrated Self-verification for Test-time Compute Scaling in Small Language Models
Paper • 2504.04718 • Published • 42 -
SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement
Paper • 2504.03561 • Published • 18 -
Concept Lancet: Image Editing with Compositional Representation Transplant
Paper • 2504.02828 • Published • 16
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 29 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 14 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 44 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 23
-
InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
Paper • 2508.18265 • Published • 207 -
SmolVLM: Redefining small and efficient multimodal models
Paper • 2504.05299 • Published • 200 -
Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
Paper • 2504.15271 • Published • 67
-
FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language
Paper • 2506.20920 • Published • 75 -
SmolVLM: Redefining small and efficient multimodal models
Paper • 2504.05299 • Published • 200 -
The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset
Paper • 2303.03915 • Published • 7 -
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper • 2502.02737 • Published • 250
-
FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language
Paper • 2506.20920 • Published • 75 -
SmolVLM: Redefining small and efficient multimodal models
Paper • 2504.05299 • Published • 200 -
YourBench: Easy Custom Evaluation Sets for Everyone
Paper • 2504.01833 • Published • 22 -
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper • 2502.02737 • Published • 250
-
The Leaderboard Illusion
Paper • 2504.20879 • Published • 72 -
SmolVLM: Redefining small and efficient multimodal models
Paper • 2504.05299 • Published • 200 -
Seedance 1.0: Exploring the Boundaries of Video Generation Models
Paper • 2506.09113 • Published • 102 -
Small Language Models are the Future of Agentic AI
Paper • 2506.02153 • Published • 21