SocraSynth: Multi-LLM Reasoning with Conditional Statistics

TL;DR

SocraSynth is a multi-LLM reasoning platform that uses conditional statistics and systematic context enhancement to improve reasoning, mitigate biases, and support human decision-making through debate and evaluation.

Contribution

The paper introduces SocraSynth, a novel multi-agent LLM reasoning system employing conditional statistics and debate mechanisms to enhance reasoning quality and address biases.

Findings

Effective in fostering rigorous research and dynamic reasoning

Enhances collaboration through adjustable contentiousness levels

Improves knowledge extraction and decision-making support

Abstract

Large language models (LLMs), while promising, face criticisms for biases, hallucinations, and a lack of reasoning capability. This paper introduces SocraSynth, a multi-LLM agent reasoning platform developed to mitigate these issues. SocraSynth utilizes conditional statistics and systematic context enhancement through continuous arguments, alongside adjustable debate contentiousness levels. The platform typically involves a human moderator and two LLM agents representing opposing viewpoints on a given subject. SocraSynth operates in two main phases: knowledge generation and reasoning evaluation. In the knowledge generation phase, the moderator defines the debate topic and contentiousness level, prompting the agents to formulate supporting arguments for their respective stances. The reasoning evaluation phase then employs Socratic reasoning and formal logic principles to appraise the quality of the arguments presented. The dialogue concludes with the moderator adjusting the contentiousness from confrontational to collaborative, gathering final, conciliatory remarks to aid in human reasoning and decision-making. Through case studies in three distinct application domains, this paper showcases SocraSynth's effectiveness in fostering rigorous research, dynamic reasoning, comprehensive assessment, and enhanced collaboration. This underscores the value of multi-agent interactions in leveraging LLMs for advanced knowledge extraction and decision-making support.