Is Multi-Agent Debate (MAD) the Silver Bullet? An Empirical Analysis of MAD in Code Summarization and Translation

• URL: http://arxiv.org/abs/2503.12029v1
• Date: Sat, 15 Mar 2025 07:30:37 GMT
• Title: Is Multi-Agent Debate (MAD) the Silver Bullet? An Empirical Analysis of MAD in Code Summarization and Translation
• Authors: Jina Chun, Qihong Chen, Jiawei Li, Iftekhar Ahmed,
• Abstract summary: Multi-Agent Debate (MAD) systems enable structured debates among Large Language Models (LLMs)<n> MAD promotes divergent thinking through role-specific agents, dynamic interactions, and structured decision-making.<n>This study investigates MAD's effectiveness on two Software Engineering (SE) tasks.
• Score: 10.038721196640864
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have advanced autonomous agents' planning and decision-making, yet they struggle with complex tasks requiring diverse expertise and multi-step reasoning. Multi-Agent Debate (MAD) systems, introduced in NLP research, address this gap by enabling structured debates among LLM-based agents to refine solutions iteratively. MAD promotes divergent thinking through role-specific agents, dynamic interactions, and structured decision-making. Recognizing parallels between Software Engineering (SE) and collaborative human problem-solving, this study investigates MAD's effectiveness on two SE tasks. We adapt MAD systems from NLP, analyze agent interactions to assess consensus-building and iterative refinement, and propose two enhancements targeting observed weaknesses. Our findings show that structured debate and collaboration improve problem-solving and yield strong performance in some cases, highlighting MAD's potential for SE automation while identifying areas for exploration.

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