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Improving LLM Reasoning with Multi-Agent Tree-of-Thought Validator Agent by Haji, Fatemeh; Bethany, Mazal; Tabar, Maryam; Chiang, Jason; Rios, Anthony; Najafirad, Peyman is a scholarly article available to read on EtoBox.

What is Improving LLM Reasoning with Multi-Agent Tree-of-Thought Validator Agent about?

Multi-agent strategies have emerged as a promising approach to enhance the reasoning abilities of Large Language Models (LLMs) by assigning specialized roles in the problem-solving process. Concurrently, Tree of Thoughts (ToT) methods have shown potential in improving reasoning for complex question-answering tasks by exploring diverse reasoning paths. A critical limitation in multi-agent reasoning is the 'Reasoner' agent's shallow exploration of reasoning paths. While ToT strategies could help mitigate this problem, they may generate flawed reasoning branches, which could harm the trustworthiness of the final answer. To leverage the strengths of both multi-agent reasoning and ToT strategies, we introduce a novel approach combining ToT-based Reasoner agents with a Thought Validator agent. Multiple Reasoner agents operate in parallel, employing ToT to explore diverse reasoning paths. The Thought Validator then scrutinizes these paths, considering a Reasoner's conclusion only if its reasoning is valid. This method enables a more robust voting strategy by discarding faulty reasoning paths, enhancing the system's ability to tackle tasks requiring systematic and trustworthy reasoning. Ou

Author

Haji, Fatemeh; Bethany, Mazal; Tabar, Maryam; Chiang, Jason; Rios, Anthony; Najafirad, Peyman

Published

2024

Language

EN