Can I read RATT: A Thought Structure for Coherent and Correct LLM Reasoning on EtoBox?

RATT: A Thought Structure for Coherent and Correct LLM Reasoning by Zhang, Jinghan; Wang, Xiting; Ren, Weijieying; Jiang, Lu; Wang, Dongjie; Liu, Kunpeng is a scholarly article available to read on EtoBox.

What is RATT: A Thought Structure for Coherent and Correct LLM Reasoning about?

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 st

Author

Zhang, Jinghan; Wang, Xiting; Ren, Weijieying; Jiang, Lu; Wang, Dongjie; Liu, Kunpeng

Published

2024

Language

EN