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DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics
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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.
Forward citations
Cited by 3 Pith papers
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CortexDebate prunes the multi-agent debate graph every round using a McKinsey-style trust score per directed link, reporting accuracy gains over full-debate baselines on eight datasets with shorter per-agent contexts.
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A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents
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