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 aggregates criterion-specific scores into a final truthfulness measure. To achieve this adaptive capability, we introduce a reward-based alignment scheme using only weak supervision from semantic consistency. At inference, we employ a multi-sampling aggregation strategy to ensure robust decisions while preserving full interpretability. We further provide theoretical analysis supporting the reliability of our approach. Extensive experiments show that HCPD consistently outperforms state-of-the-art baselines, offering an effective and explainable solution for zero-source hallucination detection. Code is available at https://github.com/TRISKEL10N/HCPD.

翻译：大语言模型常因生成事实错误或不忠实内容而产生幻觉，对其安全使用构成重大风险。在零源条件下——无法获取模型内部信息或外部参考，仅能依赖文本查询-答案对进行检测时，识别此类幻觉尤为困难。本文提出基于类人准则探测的幻觉检测范式（HCPD），该范式模拟人类评估者的多维度推理过程。其核心是类人准则探测机制（HCP），由大语言模型代理自适应地将判断分解为带权重的可解释准则集合，并通过聚合各准则评分得到最终真实性度量。为实现自适应能力，我们引入基于语义一致性的弱监督奖励对齐方案。推理阶段采用多采样聚合策略，在保持完全可解释性的同时确保决策稳健性。进一步通过理论分析验证了方法的可靠性。大量实验表明，HCPD持续优于现有基线方法，为零源幻觉检测提供了有效且可解释的解决方案。代码已开源：https://github.com/TRISKEL10N/HCPD。