RATT：用于连贯且正确的LLM推理的思维结构
RATT: A Thought Structure for Coherent and Correct LLM Reasoning
AI总结：
针对现有思维结构在复杂任务中事实检索不足和策略选择不佳的问题，提出RATT思维结构，在每步推理中结合RAG事实核查与LLM策略评估，显著提升了LLM推理的事实正确性与逻辑连贯性。
摘要：
大型语言模型（LLMs）从思维结构中获得了强大的推理和决策能力。然而，现有的方法如Tree of Thought和Retrieval Augmented Thoughts在复杂任务中常常表现不佳，原因在于对事实知识的局部检索不足以及对策略的全局选择不够充分。这些局限性使得这些方法难以在事实准确性和全面逻辑优化之间取得有效平衡。为了解决这些局限性，我们引入了Retrieval Augmented Thought Tree（RATT），这是一种新颖的思维结构，在思维过程的每一步都同时考虑整体逻辑合理性和事实正确性。具体而言，在思维分支的每个节点上，RATT执行规划和前瞻，以探索和评估多个潜在的推理步骤，并将Retrieval-Augmented Generation（RAG）的事实核查能力与LLM评估整体策略的能力相结合。通过这种事实知识与战略可行性的结合，RATT调整并整合思维树结构，以在搜索空间中寻找最有希望的分支。这种思维结构显著增强了模型在逻辑推理上的连贯性和决策效率，从而提升了LLM基于思维结构生成可靠推理和决策的能力上限。在不同类型任务上的广泛实验表明，RATT结构在事实正确性和逻辑连贯性方面显著优于现有方法。
英文摘要：
Large Language Models (LLMs) gain substantial reasoning and decision-making capabilities from thought structures. However, existing methods such as Tree of Thought and Retrieval Augmented Thoughts often fall short in complex tasks due to the limitations of insufficient local retrieval of factual knowledge and inadequate global selection of strategies. These limitations make it challenging for these methods to balance factual accuracy and comprehensive logical optimization effectively. To address these limitations, we introduce the Retrieval Augmented Thought Tree (RATT), a novel thought structure that considers both overall logical soundness and factual correctness at each step of the thinking process. Specifically, at every point of a thought branch, RATT performs planning and lookahead to explore and evaluate multiple potential reasoning steps, and integrate the fact-checking ability of Retrieval-Augmented Generation (RAG) with LLM's ability to assess overall strategy. Through this combination of factual knowledge and strategic feasibility, the RATT adjusts and integrates the thought tree structure to search for the most promising branches within the search space. This thought structure significantly enhances the model's coherence in logical inference and efficiency in decision-making, and thus increases the limit of the capacity of LLM to generate reliable inferences and decisions based on thought structures. A broad range of experiments on different types of tasks showcases that the RATT structure significantly outperforms existing methods in factual correctness and logical coherence.
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。
