用模型内部信号评估问答幻觉风险，无需额外验证
HaluNet: Learning Hallucination Risk from Internal Signals in LLM Question Answering
- 融合概率、熵和隐藏状态信息，单次生成估算幻觉风险
- 人类评估中AUPRC达0.869，高风险答案96.5%含错误
- 轻量级设计适合实时问答系统部署
大型语言模型在问答任务中表现优异，但常生成缺乏证据支持的流畅回答。现有幻觉检测方法多依赖外部验证、重复采样或测试时裁判评分，成本较高。本文提出轻量级的HaluNet，仅通过一次模型生成的内部信号（如词元概率、预测熵、隐藏状态）联合建模，输出答案级别的幻觉风险得分。该模型采用大模型自评标签作为可扩展的弱监督信号进行训练，并在SQuAD、TriviaQA和Natural Questions上通过独立人工评估与多裁判评估验证。实验表明，HaluNet在域内与域外场景下均能有效提升风险排序能力。在300条样本的人类评估中，其AUROC为0.874，AUPRC为0.869；前20%高风险答案中96.5%存在错误，相比基线错误率提升2.06倍。
原文摘要 · Abstract (English)
Large language models (LLMs) achieve strong question answering (QA) performance but can produce fluent answers unsupported by available evidence. Existing hallucination detectors often rely on external verification, repeated sampling, or test-time judge calls, which can be costly for real-time QA. We propose \textbf{HaluNet}, a lightweight hallucination risk estimator that uses internal signals from one model generation. HaluNet jointly models token likelihood, predictive entropy, and hidden-state information, allowing probabilistic, distributional, and semantic evidence to inform an answer-level risk score. It is trained with LLM-as-a-Judge labels as scalable weak supervision and evaluated with independent human and multi-judge assessments. Experiments on SQuAD, TriviaQA, and Natural Questions show that HaluNet improves answer-level risk ranking across in-domain and out-of-domain settings. On a 300-example human evaluation, HaluNet achieves 0.874 AUROC and 0.869 AUPRC; its top 20\% highest-risk answers contain 96.5\% errors, yielding a 2.06$\times$ lift over the base error rate.
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