Summary of Improving Llm Reasoning with Multi-agent Tree-of-thought Validator Agent, by Fatemeh Haji et al.
Improving LLM Reasoning with Multi-Agent Tree-of-Thought Validator Agent
by Fatemeh Haji, Mazal Bethany, Maryam Tabar, Jason Chiang, Anthony Rios, Peyman Najafirad
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to enhance the reasoning abilities of Large Language Models (LLMs) by combining Multi-Agent strategies with Tree of Thoughts (ToT) methods. The proposed method, MA-ToT, addresses the limitation of shallow exploration in multi-agent reasoning by introducing a Thought Validator agent that scrutinizes the reasoning paths generated by multiple Reasoner agents. Each Reasoner agent employs ToT to explore diverse reasoning paths, while the Thought Validator ensures that only valid conclusions are considered. This approach demonstrates superior performance compared to existing techniques on the GSM8K dataset, outperforming the standard ToT strategy by an average 5.6% across four LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers smarter and more trustworthy. It combines two ideas to help computers reason better: one idea gives them different jobs to do, and the other idea helps them explore many possible ways to solve a problem. The new approach, called MA-ToT, makes sure that only good solutions are chosen by checking the reasons behind each answer. This way, computers can be more confident in their answers. The new method was tested on a big dataset and performed better than the usual way of doing things. |
