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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 86 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 156 -
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:2501.12380
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MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
Paper • 2501.12380 • Published • 82 -
OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?
Paper • 2501.05510 • Published • 44 -
InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions
Paper • 2412.09596 • Published • 96
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Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 116 -
PaSa: An LLM Agent for Comprehensive Academic Paper Search
Paper • 2501.10120 • Published • 52 -
Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident Even When They Are Wrong
Paper • 2501.09775 • Published • 31 -
VideoWorld: Exploring Knowledge Learning from Unlabeled Videos
Paper • 2501.09781 • Published • 26
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MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models
Paper • 2501.02955 • Published • 44 -
2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining
Paper • 2501.00958 • Published • 110 -
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
Paper • 2501.12380 • Published • 82 -
VideoWorld: Exploring Knowledge Learning from Unlabeled Videos
Paper • 2501.09781 • Published • 26
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BLINK: Multimodal Large Language Models Can See but Not Perceive
Paper • 2404.12390 • Published • 27 -
TextSquare: Scaling up Text-Centric Visual Instruction Tuning
Paper • 2404.12803 • Published • 30 -
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
Paper • 2404.13013 • Published • 31 -
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
Paper • 2404.06512 • Published • 31
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GATE OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
Paper • 2411.18499 • Published • 17 -
VLSBench: Unveiling Visual Leakage in Multimodal Safety
Paper • 2411.19939 • Published • 10 -
AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?
Paper • 2412.02611 • Published • 25 -
U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs
Paper • 2412.03205 • Published • 19
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2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining
Paper • 2501.00958 • Published • 110 -
Are Vision-Language Models Truly Understanding Multi-vision Sensor?
Paper • 2412.20750 • Published • 19 -
Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs
Paper • 2412.21187 • Published • 37 -
HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs
Paper • 2412.18925 • Published • 107
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GATE OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
Paper • 2411.18499 • Published • 17 -
VLSBench: Unveiling Visual Leakage in Multimodal Safety
Paper • 2411.19939 • Published • 10 -
AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?
Paper • 2412.02611 • Published • 25 -
U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs
Paper • 2412.03205 • Published • 19
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GAIA: a benchmark for General AI Assistants
Paper • 2311.12983 • Published • 253 -
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
Paper • 2311.16502 • Published • 40 -
BLINK: Multimodal Large Language Models Can See but Not Perceive
Paper • 2404.12390 • Published • 27 -
RULER: What's the Real Context Size of Your Long-Context Language Models?
Paper • 2404.06654 • Published • 42
-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 86 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 156 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
-
GATE OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
Paper • 2411.18499 • Published • 17 -
VLSBench: Unveiling Visual Leakage in Multimodal Safety
Paper • 2411.19939 • Published • 10 -
AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?
Paper • 2412.02611 • Published • 25 -
U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs
Paper • 2412.03205 • Published • 19
-
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
Paper • 2501.12380 • Published • 82 -
OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?
Paper • 2501.05510 • Published • 44 -
InternLM-XComposer2.5-OmniLive: A Comprehensive Multimodal System for Long-term Streaming Video and Audio Interactions
Paper • 2412.09596 • Published • 96
-
Evolving Deeper LLM Thinking
Paper • 2501.09891 • Published • 116 -
PaSa: An LLM Agent for Comprehensive Academic Paper Search
Paper • 2501.10120 • Published • 52 -
Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident Even When They Are Wrong
Paper • 2501.09775 • Published • 31 -
VideoWorld: Exploring Knowledge Learning from Unlabeled Videos
Paper • 2501.09781 • Published • 26
-
2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining
Paper • 2501.00958 • Published • 110 -
Are Vision-Language Models Truly Understanding Multi-vision Sensor?
Paper • 2412.20750 • Published • 19 -
Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs
Paper • 2412.21187 • Published • 37 -
HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs
Paper • 2412.18925 • Published • 107
-
MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models
Paper • 2501.02955 • Published • 44 -
2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining
Paper • 2501.00958 • Published • 110 -
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
Paper • 2501.12380 • Published • 82 -
VideoWorld: Exploring Knowledge Learning from Unlabeled Videos
Paper • 2501.09781 • Published • 26
-
GATE OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
Paper • 2411.18499 • Published • 17 -
VLSBench: Unveiling Visual Leakage in Multimodal Safety
Paper • 2411.19939 • Published • 10 -
AV-Odyssey Bench: Can Your Multimodal LLMs Really Understand Audio-Visual Information?
Paper • 2412.02611 • Published • 25 -
U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs
Paper • 2412.03205 • Published • 19
-
BLINK: Multimodal Large Language Models Can See but Not Perceive
Paper • 2404.12390 • Published • 27 -
TextSquare: Scaling up Text-Centric Visual Instruction Tuning
Paper • 2404.12803 • Published • 30 -
Groma: Localized Visual Tokenization for Grounding Multimodal Large Language Models
Paper • 2404.13013 • Published • 31 -
InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
Paper • 2404.06512 • Published • 31
-
GAIA: a benchmark for General AI Assistants
Paper • 2311.12983 • Published • 253 -
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
Paper • 2311.16502 • Published • 40 -
BLINK: Multimodal Large Language Models Can See but Not Perceive
Paper • 2404.12390 • Published • 27 -
RULER: What's the Real Context Size of Your Long-Context Language Models?
Paper • 2404.06654 • Published • 42