Zero-source LLM Hallucination Detection with Human-like Criteria Probing
Large language models (LLMs) often hallucinate by generating factually incorrect or unfaithful content, posing significant risks to their safe use. Detecting such hallucinations is particularly challenging under the zero-source constraint, where no model internals or external references are available, and detection must rely solely on the textual query-answer pair. In this paper, we propose Human-like Criteria Probing for Hallucination Detection (HCPD), a paradigm that emulates the multi-faceted reasoning of human evaluators. Its core is a Human-like Criteria Probing (HCP) mechanism, in which a LLM agent adaptively decomposes its judgment into a weighted set of interpretable criteria and ...
| Authors | Jiahao Yang, Shuhai Zhang, Hailong Kang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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