Abstract
Large language models (LLMs) have demonstrated remarkable performance in text generation and knowledge-intensive question answering. Nevertheless, they are prone to producing hallucinated content, which severely undermines their reliability in high-stakes application domains. Existing hallucination attribution approaches, based on either external knowledge retrieval or internal model mechanisms, primarily focus on semantic similarity matching or representation-level discrimination. As a result, they have difficulty establishing structured correspondences at the span level between hallucination types, underlying error generation mechanisms, and external factual evidence, thereby limiting the interpretability of hallucinated fragments and the traceability of supporting or opposing evidence. To address these limitations, we propose HART, a fine-grained hallucination attribution and evidence retrieval framework for large language models. HART formalizes hallucination tracing as a structured modeling task comprising four stages: span localization, mechanism attribution, evidence retrieval, and causal tracing. Based upon this formulation, we develop the first structured dataset tailored for hallucination tracing, in which hallucination types, error mechanisms, and sets of counterfactual evidence are jointly annotated to enable causal-level interpretability evaluation. Experimental results on the proposed dataset demonstrate that HART substantially outperforms strong retrieval baselines, including BM25 and DPR, validating the effectiveness and generalization capability of the proposed tracing paradigm for hallucination analysis and evidence alignment.
Abstract (translated)
大型语言模型（LLMs）在文本生成和知识密集型问答方面展示了卓越的性能，但它们容易产生幻觉内容，这严重削弱了其在高风险应用场景中的可靠性。现有的基于外部知识检索或内部模型机制的幻觉归因方法主要集中在语义相似度匹配或表征层面的区别上。因此，这些方法难以建立幻觉类型、潜在错误生成机制和外部事实证据之间的结构化对应关系，从而限制了对幻觉片段的理解性和支持或反对证据的可追溯性。 为了解决这些问题，我们提出了HART（Hallucination Attribution and Retrieval for Tracing），这是一种针对大型语言模型进行细粒度幻觉归因与证据检索的框架。HART将幻觉追踪形式化为一个包含四个阶段的结构化建模任务：片段定位、机制归因、证据检索和因果追溯。基于这一公式，我们开发了第一个专门用于幻觉追踪的结构化数据集，在该数据集中共同标注幻觉类型、错误机制及反事实证据集合以实现因果层面的理解性评估。 在提出的数据集上的实验结果表明，HART显著优于包括BM25和DPR在内的强大的检索基线模型，验证了所提出追踪范式在幻觉分析和证据对齐方面的有效性和泛化能力。
URL
https://arxiv.org/abs/2603.05828
