Multi-LLM-Agents Debate - Performance, Efficiency, and Scaling Challenges

Published: 23 Jan 2025, Last Modified: 26 Feb 2025ICLR 2025 Blogpost TrackEveryoneRevisionsBibTeXCC BY 4.0
Blogpost Url: https://d2jud02ci9yv69.cloudfront.net/2025-04-28-mad-159/blog/mad/
Abstract: Multi-Agent Debate (MAD) explores leveraging collaboration among multiple large language model (LLM) agents to improve test-time performance without additional training. This blog evaluates five MAD frameworks across nine benchmarks, revealing that current MAD methods fail to consistently outperform simpler single-agent strategies, even with increased computational resources. Analysis of factors such as agent configurations and debate rounds suggests that existing MAD designs fall short in fully utilizing additional inference-time computation.
Conflict Of Interest: N/A
Submission Number: 83
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