Papers › DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics

DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics

8 Jul 2024arXiv:2407.06426archive 2025-07-28

Luke Yoffe, Alfonso Amayuelas, William Yang Wang

Multi-agent debates have been introduced to improve the accuracy of Large Language Models (LLMs) by having multiple agents discuss solutions to a problem over several rounds of debate. However, models often generate incorrect yet confident-sounding responses, which can mislead others. This issue arises partly because agents do not consider how confident their peers are. To address this, we propose DebUnc, a debate framework that uses uncertainty metrics to assess agent confidence. Confidence is then conveyed through a modified attention mechanism that adjusts token weights, or through textual prompts. Evaluations across benchmarks show that attention-based methods are particularly effective and that performance continues to improve as uncertainty estimation becomes more reliable. The code is available at https://github.com/lukeyoffe/debunc.

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