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.

通过引入检索增强思维树（RATT），结合事实知识和策略可行性，以提高大型语言模型（LLMs）的逻辑推理和决策效率。经过各种任务的广泛实验验证，RATT 在事实正确性和逻辑连贯性方面明显优于现有方法。