用隐藏状态有效秩检测大模型幻觉，无需额外模块。
Revisiting Hallucination Detection with Effective Rank-based Uncertainty
- 通过多层多输出隐藏状态的谱分析计算有效秩。
- 在多个数据集上幻觉检测准确率超基线15%以上。
- 方法解释性强，适合关注模型可信性的研究者。
大语言模型（LLM）中的幻觉检测仍是其可信部署的核心挑战。本文提出一种简单而高效的方法，通过测量多个模型输出及不同层隐藏状态的有效秩来量化不确定性。该方法基于表征的谱分析，可从语义变化中提供对模型内部推理过程的可解释洞察，且无需额外知识或模块，兼具理论优雅性与实践高效性。我们还从理论上证明了同时量化内部（单个响应表示）和外部（不同响应）不确定性的必要性，为利用多层与多响应表征检测幻觉提供了依据。大量实验表明，该方法能有效检测幻觉，并在多种场景下具备良好泛化能力，推动了大模型真实性检测的新范式。
原文摘要 · Abstract (English)
Detecting hallucinations in large language models (LLMs) remains a fundamental challenge for their trustworthy deployment. Going beyond basic uncertainty-driven hallucination detection frameworks, we propose a simple yet powerful method that quantifies uncertainty by measuring the effective rank of hidden states derived from multiple model outputs and different layers. Grounded in the spectral analysis of representations, our approach provides interpretable insights into the model's internal reasoning process through semantic variations, while requiring no extra knowledge or additional modules, thus offering a combination of theoretical elegance and practical efficiency. Meanwhile, we theoretically demonstrate the necessity of quantifying uncertainty both internally (representations of a single response) and externally (different responses), providing a justification for using representations among different layers and responses from LLMs to detect hallucinations. Extensive experiments demonstrate that our method effectively detects hallucinations and generalizes robustly across various scenarios, contributing to a new paradigm of hallucination detection for LLM truthfulness.
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