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.

翻译：大型语言模型（LLM）通过思维结构获得了显著的推理与决策能力。然而，现有方法如思维树和检索增强思维在复杂任务中往往表现不足，这源于局部事实知识检索不充分与全局策略选择不完善的局限性。这些限制使得现有方法难以有效平衡事实准确性与全面逻辑优化。为应对这些局限，我们提出了检索增强思维树（RATT），这是一种新颖的思维结构，其在思维过程的每一步均兼顾整体逻辑严谨性与事实正确性。具体而言，在思维分支的每个节点，RATT执行规划与前瞻，以探索并评估多个潜在推理步骤，并将检索增强生成（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策略可行性的结合，RATT调整并整合思维树结构，以在搜索空间中寻找最具潜力的分支。该思维结构显著增强了模型在逻辑推理中的连贯性与决策效率，从而提升了LLM基于思维结构生成可靠推理与决策的能力上限。在多种类型任务上的广泛实验表明，RATT结构在事实正确性与逻辑连贯性方面显著优于现有方法。