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Collections including paper arxiv:2403.14624

EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters

Paper • 2402.04252 • Published Feb 6 • 25
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models

Paper • 2402.03749 • Published Feb 6 • 12
ScreenAI: A Vision-Language Model for UI and Infographics Understanding

Paper • 2402.04615 • Published Feb 7 • 39
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss

Paper • 2402.05008 • Published Feb 7 • 20

DocLLM: A layout-aware generative language model for multimodal document understanding

Paper • 2401.00908 • Published Dec 31, 2023 • 181
COSMO: COntrastive Streamlined MultimOdal Model with Interleaved Pre-Training

Paper • 2401.00849 • Published Jan 1 • 14
LLaVA-Plus: Learning to Use Tools for Creating Multimodal Agents

Paper • 2311.05437 • Published Nov 9, 2023 • 48
LLaVA-Interactive: An All-in-One Demo for Image Chat, Segmentation, Generation and Editing

Paper • 2311.00571 • Published Nov 1, 2023 • 41

MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?

Paper • 2403.14624 • Published Mar 21 • 51
AI4Math/MathVerse

Viewer • Updated Apr 19 • 4.73k • 976 • 41
CaraJ/MathVerse-lmmseval

Viewer • Updated Apr 19 • 8.67k • 802 • 2
CaraJ/Mathverse_VLMEvalKit

Viewer • Updated Sep 2 • 8.67k • 79

Jamba: A Hybrid Transformer-Mamba Language Model

Paper • 2403.19887 • Published Mar 28 • 104
sDPO: Don't Use Your Data All at Once

Paper • 2403.19270 • Published Mar 28 • 40
ViTAR: Vision Transformer with Any Resolution

Paper • 2403.18361 • Published Mar 27 • 52
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models

Paper • 2403.18814 • Published Mar 27 • 44

MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?

Paper • 2403.14624 • Published Mar 21 • 51

Papers - Benchmarks - Math

MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?

Paper • 2403.14624 • Published Mar 21 • 51
Challenge LLMs to Reason About Reasoning: A Benchmark to Unveil Cognitive Depth in LLMs

Paper • 2312.17080 • Published Dec 28, 2023 • 1
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?

Paper • 2407.01284 • Published Jul 1 • 75
DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models

Paper • 2411.00836 • Published Oct 29 • 15

Papers - Image - Math

MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?

Paper • 2403.14624 • Published Mar 21 • 51
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning?

Paper • 2407.01284 • Published Jul 1 • 75
MAVIS: Mathematical Visual Instruction Tuning

Paper • 2407.08739 • Published Jul 11 • 30

MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?

Paper • 2403.14624 • Published Mar 21 • 51

Unlocking the conversion of Web Screenshots into HTML Code with the WebSight Dataset

Paper • 2403.09029 • Published Mar 14 • 54
LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression

Paper • 2403.12968 • Published Mar 19 • 24
RAFT: Adapting Language Model to Domain Specific RAG

Paper • 2403.10131 • Published Mar 15 • 67
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking

Paper • 2403.09629 • Published Mar 14 • 74

Papers - CoT - Chain of Thought

Contrastive Decoding Improves Reasoning in Large Language Models

Paper • 2309.09117 • Published Sep 17, 2023 • 37
Chain-of-Thought Reasoning Without Prompting

Paper • 2402.10200 • Published Feb 15 • 102
MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?

Paper • 2403.14624 • Published Mar 21 • 51
Chain of Thought Empowers Transformers to Solve Inherently Serial Problems

Paper • 2402.12875 • Published Feb 20 • 13

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