Literature Review Of Multi-Agent Debate For Problem-Solving

TL;DR

This literature review examines recent advances in multi-agent large language models, highlighting their potential for improved problem-solving, current challenges, and the need for direct comparative studies to guide future research.

Contribution

It synthesizes recent research on MA-LLMs, analyzing factors like communication and decision-making, and identifies key challenges and directions for future development.

Findings

Multi-agent systems outperform single-agent models in complex tasks.

Scalability and communication structure significantly impact MA-LLM performance.

Current challenges include high computational costs and under-explored issues.

Abstract

Multi-agent large language models (MA-LLMs) are a rapidly growing research area that leverages multiple interacting language agents to tackle complex tasks, outperforming single-agent large language models. This literature review synthesizes the latest research on agent profiles, communication structures, and decision-making processes, drawing insights from both traditional multi-agent systems and state-of-the-art MA-LLM studies. In doing so, it aims to address the lack of direct comparisons in the field, illustrating how factors like scalability, communication structure, and decision-making processes influence MA-LLM performance. By examining frequent practices and outlining current challenges, the review reveals that multi-agent approaches can yield superior results but also face elevated computational costs and under-explored challenges unique to MA-LLM. Overall, these findings provide researchers and practitioners with a roadmap for developing robust and efficient multi-agent AI solutions.