Collections
Discover the best community collections!
Collections including paper arxiv:2402.03620
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BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Paper • 2402.04291 • Published • 49 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 116 -
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
Paper • 2402.04248 • Published • 31 -
Scaling Laws for Downstream Task Performance of Large Language Models
Paper • 2402.04177 • Published • 18
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Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 116 -
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 105 -
Orca-Math: Unlocking the potential of SLMs in Grade School Math
Paper • 2402.14830 • Published • 24 -
Teaching Large Language Models to Reason with Reinforcement Learning
Paper • 2403.04642 • Published • 46
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Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 19 -
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
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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
Efficient Tool Use with Chain-of-Abstraction Reasoning
Paper • 2401.17464 • Published • 19 -
ReFT: Reasoning with Reinforced Fine-Tuning
Paper • 2401.08967 • Published • 30 -
The Impact of Reasoning Step Length on Large Language Models
Paper • 2401.04925 • Published • 17