-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 153 -
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:2510.04849
-
Demystifying Reinforcement Learning in Agentic Reasoning
Paper • 2510.11701 • Published • 33 -
Self-Improving LLM Agents at Test-Time
Paper • 2510.07841 • Published • 10 -
Making Mathematical Reasoning Adaptive
Paper • 2510.04617 • Published • 23 -
DocReward: A Document Reward Model for Structuring and Stylizing
Paper • 2510.11391 • Published • 27
-
Chain-of-Verification Reduces Hallucination in Large Language Models
Paper • 2309.11495 • Published • 40 -
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 82 -
CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages
Paper • 2309.09400 • Published • 87 -
Language Modeling Is Compression
Paper • 2309.10668 • Published • 85
-
The Path Not Taken: RLVR Provably Learns Off the Principals
Paper • 2511.08567 • Published • 35 -
Reasoning with Sampling: Your Base Model is Smarter Than You Think
Paper • 2510.14901 • Published • 48 -
When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQA
Paper • 2510.04849 • Published • 117
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 30 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
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
-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 153 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
-
The Path Not Taken: RLVR Provably Learns Off the Principals
Paper • 2511.08567 • Published • 35 -
Reasoning with Sampling: Your Base Model is Smarter Than You Think
Paper • 2510.14901 • Published • 48 -
When Models Lie, We Learn: Multilingual Span-Level Hallucination Detection with PsiloQA
Paper • 2510.04849 • Published • 117
-
Demystifying Reinforcement Learning in Agentic Reasoning
Paper • 2510.11701 • Published • 33 -
Self-Improving LLM Agents at Test-Time
Paper • 2510.07841 • Published • 10 -
Making Mathematical Reasoning Adaptive
Paper • 2510.04617 • Published • 23 -
DocReward: A Document Reward Model for Structuring and Stylizing
Paper • 2510.11391 • Published • 27
-
EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 30 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 15 -
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
-
Chain-of-Verification Reduces Hallucination in Large Language Models
Paper • 2309.11495 • Published • 40 -
Adapting Large Language Models via Reading Comprehension
Paper • 2309.09530 • Published • 82 -
CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages
Paper • 2309.09400 • Published • 87 -
Language Modeling Is Compression
Paper • 2309.10668 • Published • 85