链还是树？从思维矩阵的视角重新评估复杂推理
Chain or tree? Re-evaluating complex reasoning from the perspective of a matrix of thought
AI总结：
针对LLM复杂推理中思维结构冗余与路径单一问题，提出思维矩阵（MoT），通过列-单元通信和事实校正机制，在24点、问答等任务上以14.4%的推理时间超越现有方法。
摘要：
大型语言模型（LLMs）在处理复杂和抽象任务时，由于推理能力不足，面临显著的准确性下降问题。思维链（CoT）和思维树（ToT）等思维结构专注于增强LLMs的推理能力。然而，它们存在固有的缺点，如树结构同一层内的冗余以及链结构路径的单一性。一些研究利用检索增强生成（RAG）方法来增强CoT和ToT，以缓解LLMs中的幻觉，但思维结构的根本缺陷仍然存在。此外，在处理多实体和多跳信息时，检索到的验证知识往往包含大量碎片化、表面化甚至错误的数据，误导LLMs的推理过程。为解决这些问题，我们提出了思维矩阵（MoT），一种新颖且高效的LLMs思维结构。MoT通过“列-单元通信”机制在水平和垂直两个维度上探索问题，使LLMs能够积极参与多策略和深度思考，同时减少列单元内思维节点的冗余，从而增强LLMs的推理能力。此外，通过事实校正机制，它利用RAG检索到的知识图谱三元组和原始文本来构建知识单元并纠正错误答案。为验证该方法的有效性，我们在三个任务中进行了大量实验：24点游戏、问答评估和命题写作。结果表明，我们的框架优于最先进的方法，推理时间仅为基线方法的14.4%，证明了其效率和准确性。框架代码可在https://github.com/lyfiter/mtqa获取。
英文摘要：
Large Language Models (LLMs) face significant accuracy degradation due to insufficient reasoning ability when dealing with complex and abstract tasks. Thought structures such as Chain of Thought (CoT) and Tree of Thought (ToT) focus on enhancing the reasoning capability of LLMs. However, they suffer from inherent drawbacks such as redundancy within the same layer of the tree structure and the singularity of the paths in the chain structure. Some studies have utilized Retrieval-Augmented Generation (RAG) methods to enhance CoT and ToT in mitigating hallucinations in LLMs, yet the fundamental shortcomings of the thought structures still persist. Furthermore, when dealing with multi-entity and multi-hop information, the retrieved verification knowledge often contains large amounts of fragmented, superficial, or even erroneous data, misleading the reasoning process of LLMs. To address these issues, we propose the Matrix of Thought (MoT), a novel and efficient thought structure for LLMs. MoT explores problems in both horizontal and vertical dimensions through a "column-cell communication" mechanism, enabling LLMs to actively engage in multi-strategy and deep thinking while reducing redundancy in the thought nodes within the column cells, thereby enhancing the reasoning capability of LLMs. Additionally, through a fact-correction mechanism, it leverages the knowledge graph triples retrieved by RAG and the original text to construct knowledge units and correct erroneous answers. To validate the effectiveness of this method, we conducted extensive experiments in three tasks: 24-point game, question answering evaluation, and proposition writing.The results demonstrate that our framework outperforms state-of-the-art methods, with reasoning time only 14.4\% of that of the baseline method, proving its efficiency and accuracy. The code for framework is available at https://github.com/lyfiter/mtqa.
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。
