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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 21 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 78 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 140 -
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:2402.10200
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CRUXEval: A Benchmark for Code Reasoning, Understanding and Execution
Paper • 2401.03065 • Published • 10 -
DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligence
Paper • 2401.14196 • Published • 46 -
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation
Paper • 2312.14187 • Published • 49 -
On the Effectiveness of Large Language Models in Domain-Specific Code Generation
Paper • 2312.01639 • Published • 1
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Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs
Paper • 2407.00653 • Published • 11 -
Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs
Paper • 2406.18629 • Published • 38 -
Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities
Paper • 2406.14562 • Published • 27 -
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models
Paper • 2406.04271 • Published • 27
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Plot2Code: A Comprehensive Benchmark for Evaluating Multi-modal Large Language Models in Code Generation from Scientific Plots
Paper • 2405.07990 • Published • 16 -
Large Language Models as Planning Domain Generators
Paper • 2405.06650 • Published • 9 -
AutoCrawler: A Progressive Understanding Web Agent for Web Crawler Generation
Paper • 2404.12753 • Published • 41 -
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
Paper • 2404.07972 • Published • 43
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Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 94 -
Self-Discover: Large Language Models Self-Compose Reasoning Structures
Paper • 2402.03620 • Published • 109 -
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences
Paper • 2404.03715 • Published • 59 -
Do language models plan ahead for future tokens?
Paper • 2404.00859 • Published • 2
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Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 102 -
sDPO: Don't Use Your Data All at Once
Paper • 2403.19270 • Published • 38 -
ViTAR: Vision Transformer with Any Resolution
Paper • 2403.18361 • Published • 51 -
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Paper • 2403.18814 • Published • 44
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Linearity of Relation Decoding in Transformer Language Models
Paper • 2308.09124 • Published • 2 -
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 94 -
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 103 -
Mission: Impossible Language Models
Paper • 2401.06416 • Published • 3
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Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Paper • 1701.06538 • Published • 4 -
Attention Is All You Need
Paper • 1706.03762 • Published • 41 -
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Paper • 2005.11401 • Published • 11 -
Language Model Evaluation Beyond Perplexity
Paper • 2106.00085 • Published