Talk Isn’t Always Cheap
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Multi-agent debate does not always help. Across three reasoning benchmarks and heterogeneous agent pools, debate often lowers accuracy, with stronger models sometimes swayed into worse answers by weaker peers. The authors argue that current alignment makes agents too agreeable, so they adopt persuasive but wrong reasoning instead of challenging it.
Setup. Evaluate debate on CommonSenseQA, MMLU, and GSM8K using GPT-4o-mini, Llama-3.1-8B-Instruct, and Mistral-7B-Instruct. Agents answer once, then debate for two rounds; final output is a majority vote pre- vs post-debate. Prompts require short reasoning and task-specific formats.
Main result. Debate frequently hurts accuracy, especially on CommonSenseQA and MMLU. They show consistent drops after debate for many groups, including mixed-capability settings: e.g., CSQA falls by 6.6 points for 1×GPT + 2×Llama and by 8.0 points for 2×Llama + 1×Mistral; MMLU drops by 12.0 points for 1×GPT + 2×Llama. GSM8K is more mixed, with small gains in some settings.
Degradation over rounds. This work tracks accuracy across rounds and shows performance often declining as debate proceeds, even when stronger models are in the majority.
Why it happens. Agents tend to favor agreement over critique. They reveal more correct→incorrect flips than incorrect→correct flips across rounds, indicating that debate can actively mislead stronger models. Appendix examples document sycophantic reversals from correct to wrong answers after reading peers.
Implications. Naive debate protocols risk amplifying errors. The authors recommend designs that reward independent verification, weight arguments by agent credibility or confidence, and penalize unjustified agreement to preserve the benefits of discussion.
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