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Collections including paper arxiv:2406.04271

Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs

Paper • 2407.00653 • Published Jun 30 • 11
Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs

Paper • 2406.18629 • Published Jun 26 • 40
Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities

Paper • 2406.14562 • Published Jun 20 • 27
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

Paper • 2406.04271 • Published Jun 6 • 27

Mixture-of-Agents Enhances Large Language Model Capabilities

Paper • 2406.04692 • Published Jun 7 • 55
CRAG -- Comprehensive RAG Benchmark

Paper • 2406.04744 • Published Jun 7 • 41
Boosting Large-scale Parallel Training Efficiency with C4: A Communication-Driven Approach

Paper • 2406.04594 • Published Jun 7 • 4
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

Paper • 2406.04271 • Published Jun 6 • 27

Autoregressive Model Beats Diffusion: Llama for Scalable Image Generation

Paper • 2406.06525 • Published Jun 10 • 64
Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning

Paper • 2406.06469 • Published Jun 10 • 23
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

Paper • 2406.04271 • Published Jun 6 • 27
Block Transformer: Global-to-Local Language Modeling for Fast Inference

Paper • 2406.02657 • Published Jun 4 • 36

Papers - Chain of Thoughts - Multi-Shot - Buffer of Thoughts

Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

Paper • 2406.04271 • Published Jun 6 • 27

Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

Paper • 2406.04271 • Published Jun 6 • 27
AgentGym: Evolving Large Language Model-based Agents across Diverse Environments

Paper • 2406.04151 • Published Jun 6 • 17
Open-Endedness is Essential for Artificial Superhuman Intelligence

Paper • 2406.04268 • Published Jun 6 • 11

The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions

Paper • 2404.13208 • Published Apr 19 • 38
Mixture-of-Agents Enhances Large Language Model Capabilities

Paper • 2406.04692 • Published Jun 7 • 55
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

Paper • 2406.04271 • Published Jun 6 • 27

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 • 72

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 • 99
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

Can large language models explore in-context?

Paper • 2403.15371 • Published Mar 22 • 32
Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models

Paper • 2406.04271 • Published Jun 6 • 27
CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis

Paper • 2407.13301 • Published Jul 18 • 54
WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agents

Paper • 2410.07484 • Published Oct 9 • 48

Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30 • 16
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation

Paper • 2401.15688 • Published Jan 28 • 11
SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Paper • 2401.15024 • Published Jan 26 • 68
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities

Paper • 2401.15071 • Published Jan 26 • 34

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