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MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Paper • 2405.07526 • Published • 21 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 17 -
A Touch, Vision, and Language Dataset for Multimodal Alignment
Paper • 2402.13232 • Published • 17 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 31
Collections
Discover the best community collections!
Collections including paper arxiv:2504.11456
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OpenAI o1 System Card
Paper • 2412.16720 • Published • 38 -
LearnLM: Improving Gemini for Learning
Paper • 2412.16429 • Published • 23 -
NILE: Internal Consistency Alignment in Large Language Models
Paper • 2412.16686 • Published • 8 -
Offline Reinforcement Learning for LLM Multi-Step Reasoning
Paper • 2412.16145 • Published • 38
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MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Paper • 2405.07526 • Published • 21 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 17 -
A Touch, Vision, and Language Dataset for Multimodal Alignment
Paper • 2402.13232 • Published • 17 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 31
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URSA: Understanding and Verifying Chain-of-thought Reasoning in Multimodal Mathematics
Paper • 2501.04686 • Published • 53 -
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
Paper • 2501.09686 • Published • 41 -
LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
Paper • 2411.10440 • Published • 133 -
TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding
Paper • 2502.19400 • Published • 47
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MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Paper • 2405.07526 • Published • 21 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 17 -
A Touch, Vision, and Language Dataset for Multimodal Alignment
Paper • 2402.13232 • Published • 17 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 31
-
URSA: Understanding and Verifying Chain-of-thought Reasoning in Multimodal Mathematics
Paper • 2501.04686 • Published • 53 -
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models
Paper • 2501.09686 • Published • 41 -
LLaVA-CoT: Let Vision Language Models Reason Step-by-Step
Paper • 2411.10440 • Published • 133 -
TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding
Paper • 2502.19400 • Published • 47
-
OpenAI o1 System Card
Paper • 2412.16720 • Published • 38 -
LearnLM: Improving Gemini for Learning
Paper • 2412.16429 • Published • 23 -
NILE: Internal Consistency Alignment in Large Language Models
Paper • 2412.16686 • Published • 8 -
Offline Reinforcement Learning for LLM Multi-Step Reasoning
Paper • 2412.16145 • Published • 38
-
MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Paper • 2405.07526 • Published • 21 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 17 -
A Touch, Vision, and Language Dataset for Multimodal Alignment
Paper • 2402.13232 • Published • 17 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 31