Literature Review Of Multi-Agent Debate For Problem-Solving
It addresses the lack of direct comparisons in MA-LLM research for researchers and practitioners, though it is incremental as a review paper.
This literature review synthesizes research on multi-agent large language models (MA-LLMs) for complex problem-solving, finding that they outperform single-agent approaches but face higher computational costs and under-explored challenges.
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.