研究：RAG 引文失实，1.2%–15.7% 的可引用论断其实来自参数记忆

论文进一步发现，NLI 验证这一标准方法的结果与随机猜测无异，即便把检查范围限制在被引用的段落内也如此。作者定位了结构性成因：从记忆作答的模型会选择一条与其输出一致的引文。在相同论断上对比十种归因方法后，有上下文与无上下文时模型输出分布的差异最能区分来源，但基于该信号构建的检测器仍不足以实用——它度量的是模型知道什么而非实际使用了什么，二者的分离仍是未解决问题。

Cited but Unused: Parametric Provenance in Retrieval-Augmented Generation

Retrieval-augmented generation systems attach citations to attribute claims to their sources. These citations frequently misstate provenance, and standard verification cannot detect this. We measure provenance by counterfactual value substitution: a fact in the retrieved passages is replaced with a false variant and the model re-queried. We find that across five sizes of one model family and a second comparator family, 1.2% to 15.7% of citable claims derive from parametric memory rather than the retrieved context. At the most conservative substitution distance these figures are 4.6% and 8.5% on the two models we tested at it, with the rate rising with model size within a family. We further find that natural language inference verification, the standard approach, gives results indistinguishable from chance across models, even when restricted to the cited passage. We determine that the cause is structural: a model answering from memory selects a citation that agrees with its output. Comparing ten attribution methods on identical claims, the contrast between the model's distribution with and without context best separates provenance. Acting on that signal does not improve provenance honesty at these base rates. The resulting detector is hence still not accurate enough to act on: it measures what the model knew rather than what it used, and separating those remains the open problem.

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信源核验说明：本文原始线索采编自「arXiv cs.AI」（@Tanzeel Abbas, Tehseen Zia, David Windridge），经 AITOP 多维评分管线结构化提炼。