D eb U nc: Improving Large Language Model Agent Communication With Uncertainty Metrics (2025)

Abstract: 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 the 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 textual prompts or via a modified attention mechanism that adjusts token weights. 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.

## introduction large language models (llms) have demonstrated impressive performance across various domains, including law, academia, and coding @xcite . however, they are prone to hallucinations, where they confidently generate false or misleading information @xcite . this poses significant risks in real-world applications. for example, an llm tutor providing incorrect explanations could mislead students, while a customer service agent giving faulty advice could frustrate users. in highstakes fields like healthcare, journalism, or finance, hallucinations can have severe consequences, including financial loss or health risks. to mitigate these issues, researchers have explored multi-agent debate, where multiple llms propose diverse solutions and critique each other's reasoning over several rounds of debate @xcite . this process has been shown to enhance the reasoning and accuracy of llms, outperforming simpler approaches such as majority voting or chain-ofthought prompting @xcite . ## related work llms are prone to overconfidence and often generate responses regardless of their certainty. this leads to hallucinations, where the information provided by the model is incorrect or unsupported by its training data @xcite @xcite @xcite . factual accuracy is crucial for building trust in llm-based systems and enabling real-world applications. recent work has focused on understanding the causes of hallucinations and developing strategies to mitigate them @xcite @xcite . ## method in multi-agent llm debates, agents often produce confident-sounding yet inaccurate responses, potentially misleading other agents and leading to consensus on incorrect answers @xcite . our goal is to guide agents on which opinions to prioritize based on uncertainty levels. our modified debate pipeline, illustrated in figure 2 , operates as follows: in each round of debate, every agent generates a response and its uncertainty is assessed by an uncertainty metric. in the subsequent round, each agent shares its response and uncertainty with all other agents. we evaluate on three uncertainty metrics and three methods for communicating agent uncertainty. ## results in this section, we first analyze the effectiveness of each uncertainty incorporation method, and then analyze the effectiveness of the uncertainty metrics. ## conclusion we introduce debunc, a framework that integrates model uncertainty into multi-agent debates. by incorporating confidence levels, agents can assess the reliability of their peers and make more informed decisions when responses differ. confidence is estimated using an uncertainty metric and conveyed through either attention scaling, which adjusts token weights based on confidence levels, or direct inclusion in prompts. our results show that attention scaling outperforms textual prompts, highlighting that llms can process information beyond traditional text inputs. this technique is broadly applicable to scenarios where certain parts of the input should be prioritized, and we encourage further exploration of its potential.