DebUnc: Improving Large Language Model Agent Communication Via Uncertainty Metrics
Keywords: multiagent debate, model uncertainty, agent communication, large language models
TL;DR: This paper demonstrates how integrating uncertainty metrics into multi-agent debates can reduce the likelihood of agents being misled by hallucinations, improving the reliability of large language models.
Abstract: To enhance Large Language Model (LLM) capabilities, multi-agent debates have been introduced, where multiple LLMs discuss solutions to a problem over several rounds of debate. However, LLMs often produce incorrect responses that appear confident, which can mislead other agents. This is partly because agents do not express their confidence levels during standard debates. To address this, we introduce DebUnc, a multi-agent debate framework that uses uncertainty metrics to assess agent confidence levels. We adapted the LLM attention mechanism to adjust token weights based on confidence levels and also explored using textual prompts to convey confidence. Our evaluations across various benchmarks show that attention-based methods are particularly effective, and that as uncertainty metrics improve, performance will continue to increase.
Supplementary Material: zip
Primary Area: generative models
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Submission Number: 13166
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