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2024

RATT: A Thought Structure for Coherent and Correct LLM Reasoning

Zhang, Jinghan, Wang, Xiting, Ren, Weijieying et al.

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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.

RATT: A Thought Structure for Coherent and Correct LLM Reasoning · Around

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