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Iterative Reasoning Preference Optimization
Paper • 2404.19733 • Published • 48 -
Better & Faster Large Language Models via Multi-token Prediction
Paper • 2404.19737 • Published • 75 -
ORPO: Monolithic Preference Optimization without Reference Model
Paper • 2403.07691 • Published • 64 -
KAN: Kolmogorov-Arnold Networks
Paper • 2404.19756 • Published • 109
Collections
Discover the best community collections!
Collections including paper arxiv:2406.14491
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sDPO: Don't Use Your Data All at Once
Paper • 2403.19270 • Published • 41 -
Advancing LLM Reasoning Generalists with Preference Trees
Paper • 2404.02078 • Published • 44 -
Learn Your Reference Model for Real Good Alignment
Paper • 2404.09656 • Published • 83 -
mDPO: Conditional Preference Optimization for Multimodal Large Language Models
Paper • 2406.11839 • Published • 38
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InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions
Paper • 2401.13313 • Published • 5 -
BAAI/Bunny-v1_0-4B
Text Generation • Updated • 37 • 9 -
What matters when building vision-language models?
Paper • 2405.02246 • Published • 102 -
Jina CLIP: Your CLIP Model Is Also Your Text Retriever
Paper • 2405.20204 • Published • 35
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A Survey on Data Selection for LLM Instruction Tuning
Paper • 2402.05123 • Published • 3 -
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
Paper • 2312.14187 • Published • 50 -
Generative Representational Instruction Tuning
Paper • 2402.09906 • Published • 54 -
Instruction-tuned Language Models are Better Knowledge Learners
Paper • 2402.12847 • Published • 26
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World Model on Million-Length Video And Language With RingAttention
Paper • 2402.08268 • Published • 38 -
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 80 -
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 105 -
FiT: Flexible Vision Transformer for Diffusion Model
Paper • 2402.12376 • Published • 48
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Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 18 -
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation
Paper • 2401.15688 • Published • 11 -
SliceGPT: Compress Large Language Models by Deleting Rows and Columns
Paper • 2401.15024 • Published • 72 -
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities
Paper • 2401.15071 • Published • 37