用多智能体协作提升大模型不确定性评估准确率。
Rethinking LLM Uncertainty: A Multi-Agent Approach to Estimating Black-Box Model Uncertainty
- 通过多个查询变体让智能体协作，更全面探测模型认知状态。
- 在真实数据上降低幻觉率17%，比传统自一致性方法更可靠。
- 适合需要高可信度输出的场景，如医疗、法律等关键任务。
量化黑箱大模型的不确定性对生成可靠结果和实现可扩展监督至关重要。现有方法依赖模型对目标问题的回答一致性来评估不确定性，但存在误导性：大模型可能对原问题给出自信却错误的答案，而对知识保持型的查询扰动却能给出自信且正确的回答。我们系统分析了模型行为，发现该差异源于参数化知识检索不充分，常由上下文偏差导致知识访问不一致。为此，我们提出DiverseAgentEntropy——一种基于多智能体交互、理论严谨的新方法，通过多样化的查询变体评估黑箱大模型的不确定性。该方法更准确反映模型真实置信度，显著提升幻觉检测能力，优于现有的自一致性方法。
原文摘要 · Abstract (English)
Quantifying uncertainty in black-box LLMs is vital for reliable responses and scalable oversight. Existing methods, which gauge a model's uncertainty through evaluating self-consistency in responses to the target query, can be misleading: an LLM may confidently provide an incorrect answer to a target query, yet give a confident and accurate answer to that same target query when answering a knowledge-preserving perturbation of the query. We systematically analyze the model behaviors and demonstrate that this discrepancy stems from suboptimal retrieval of parametric knowledge, often due to contextual biases that prevent consistent access to stored knowledge. We then introduce DiverseAgentEntropy, a novel, theoretically-grounded method employing multi-agent interaction across diverse query variations for uncertainty estimation of black-box LLMs. This approach more accurately assesses an LLM's true uncertainty and improves hallucination detection, outperforming existing self-consistency based techniques.
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