Multi-agent debate degrades generation but boosts error detection in data cleaning; a derived benefit condition predicts outcomes across tasks and generalizes to other domains.
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Humans chatting with an unreliable LLM assistant outperform both the model alone and unaided humans on MMLU and time-limited QuALITY tasks.
citing papers explorer
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When Helping Hurts and How to Fix It: Multi-Agent Debate for Data Cleaning
Multi-agent debate degrades generation but boosts error detection in data cleaning; a derived benefit condition predicts outcomes across tasks and generalizes to other domains.
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Measuring Progress on Scalable Oversight for Large Language Models
Humans chatting with an unreliable LLM assistant outperform both the model alone and unaided humans on MMLU and time-limited QuALITY tasks.