首个基于真实人机对话的幻觉检测基准，揭示60%数学问题存在幻觉。
Detecting Hallucinations in Authentic LLM-Human Interactions
- 从真实用户对话中收集并标注幻觉样本，构建权威基准
- 31.4%对话对含幻觉，数学类高达60%
- 验证普通大模型自检能力不足，适合安全敏感领域研究者
随着大语言模型（LLMs）在医疗、法律等敏感领域广泛应用，幻觉检测成为关键任务。尽管已有诸多基准推动该领域研究，但多数为人工构造——或刻意诱导幻觉，或模拟交互——而非源自真实人机对话。因此，这些基准难以全面反映实际使用中的幻觉特征。为解决此问题，我们提出首个完全基于真实人机对话构建的幻觉检测基准AuthenHallu。通过对真实对话数据进行筛选与标注，该基准忠实呈现了大模型在日常使用中的幻觉表现。统计分析显示，本基准中31.4%的问答对包含幻觉，且在数学与数值问题等挑战性领域这一比例飙升至60.0%。此外，我们探索使用原始大模型自身作为幻觉检测器的潜力，发现其当前性能在真实场景中仍不足够。相关数据与代码已公开于https://github.com/TAI-HAMBURG/AuthenHallu。
原文摘要 · Abstract (English)
As large language models (LLMs) are increasingly applied in sensitive domains such as medicine and law, hallucination detection has become a critical task. Although numerous benchmarks have been proposed to advance research in this area, most of them are artificially constructed--either through deliberate hallucination induction or simulated interactions--rather than derived from genuine LLM-human dialogues. Consequently, these benchmarks fail to fully capture the characteristics of hallucinations that occur in real-world usage. To address this limitation, we introduce AuthenHallu, the first hallucination detection benchmark built entirely from authentic LLM-human interactions. For AuthenHallu, we select and annotate samples from genuine LLM-human dialogues, thereby providing a faithful reflection of how LLMs hallucinate in everyday user interactions. Statistical analysis shows that hallucinations occur in 31.4% of the query-response pairs in our benchmark, and this proportion increases dramatically to 60.0% in challenging domains such as Math & Number Problems. Furthermore, we explore the potential of using vanilla LLMs themselves as hallucination detectors and find that, despite some promise, their current performance remains insufficient in real-world scenarios. The data and code are publicly available at https://github.com/TAI-HAMBURG/AuthenHallu.
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