CoAT：用于增强大语言模型推理能力的关联思维链框架
CoAT: Chain-of-Associated-Thoughts Framework for Enhancing Large Language Models Reasoning
AI总结：
针对大语言模型快思考推理的局限，本文提出融合MCTS与联想记忆机制的CoAT慢思考推理框架，在多类推理数据集上实现显著性能提升。
摘要：
大语言模型（LLM）技术研究正快速兴起，其中多数技术采用“快思考”方式进行推理，即大多数LLM仅基于单次查询和自身推理能力生成最终结果。不过随着OpenAI-o1的出现，“慢思考”技术因推理过程更贴近人类思维过程而受到越来越多的关注。受人类在思考过程中会不断关联、补充知识的能力启发，我们提出了全新的关联思维链（Chain-of-Associated-Thoughts, CoAT）框架，该框架实现了蒙特卡洛树搜索（Monte Carlo Tree Search, MCTS）算法与名为“联想记忆”的动态关键新信息整合机制的创新协同。通过结合MCTS的结构化探索能力与联想记忆的自适应学习能力，CoAT大幅拓展了LLM的搜索空间，使框架能够探索多样化的推理路径，并实时动态更新知识库。这让框架不仅可以回溯、优化先前的推理结论，还能自适应整合不断演化的信息，确保最终输出准确且全面。我们在多种生成与推理任务上验证了CoAT的有效性，量化实验显示，CoAT在开源多跳推理数据集（HotpotQA、MuSiQue）上实现了超过10%的性能提升，在自研CRB数据集上取得了超过15%的性能增益。
英文摘要：
Research on LLM technologies is rapidly emerging, with most of them employ a 'fast thinking' approach to inference. Most LLMs generate the final result based solely on a single query and LLM's reasoning capabilities. However, with the advent of OpenAI-o1, 'slow thinking' techniques have garnered increasing attention because its process is closer to the human thought process. Inspired by the human ability to constantly associate and replenish knowledge during thinking, we developed the novel Chain-of-Associated-Thoughts (CoAT) framework, which introduces an innovative synergy between the Monte Carlo Tree Search (MCTS) algorithm and a dynamic mechanism for integrating new key information, termed 'associative memory'. By combining the structured exploration capabilities of MCTS with the adaptive learning capacity of associative memory, CoAT significantly expands the LLM search space, enabling our framework to explore diverse reasoning pathways and dynamically update its knowledge base in real-time. This allows the framework to not only revisit and refine earlier inferences but also adaptively incorporate evolving information, ensuring that the final output is both accurate and comprehensive. We validate CoAT's effectiveness across a variety of generative and reasoning tasks. Quantitative experiments show that CoAT achieves over 10% performance improvement on open-source multi-hop reasoning datasets (HotpotQA, MuSiQue) and more than 15% gain on our proprietary CRB dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
