用多重检验方法提升大模型幻觉检测的可靠性。
Principled Detection of Hallucinations in Large Language Models via Multiple Testing
- 将幻觉检测建模为假设检验，通过校准p值融合多评分
- 在多个模型和数据集上实现低误报率的稳定检测
- 适合需要可信检测结果的研究者和应用开发者
尽管大语言模型在解决各类任务中表现出强大能力，但其易产生幻觉——即生成看似自信却实际错误或荒谬的内容。现有幻觉检测方法依赖多种经验性评分规则，但性能在不同模型和数据集间波动大，难以判断哪些方法可靠。本文将幻觉检测问题形式化为假设检验，并借鉴机器学习中分布外检测的思路，提出一种基于多重检验的检测方法，通过置信区间的校准p值系统性聚合多个评估分数，实现可控误报率的可靠检测。在多个模型与数据集上的大量实验验证了该方法对现有先进方法的鲁棒性优势。
原文摘要 · Abstract (English)
While Large Language Models (LLMs) have emerged as powerful foundational models to solve a variety of tasks, they have also been shown to be prone to hallucinations, i.e., generating responses that sound confident but are actually incorrect or even nonsensical. Existing hallucination detectors propose a wide range of empirical scoring rules, but their performance varies across models and datasets, and it is hard to determine which ones to rely on in practice or to treat as a reliable detector. In this work, we formulate the problem of detecting hallucinations as a hypothesis testing problem and draw parallels with the problem of out-of-distribution detection in machine learning models. We then propose a multiple-testing-inspired method that systematically aggregates multiple evaluation scores via conformal p-values, enabling calibrated detection with controlled false alarm rate. Extensive experiments across diverse models and datasets validate the robustness of our approach against state-of-the-art methods.
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