CLADE：检索增强语言模型在证据粒度不足时的类别级作答现象
CLADE: Category-Level Answering from Deficient Evidence in Retrieval-Augmented Language Models
AI 导读
arXiv 论文提出“可解决的不足”概念：检索增强系统取回的段落相关但粒度过粗，语言模型据此作答而非弃答，作者称之为 Clade。在 PrimeKG 生物医学问答上，仅含疾病家族的段落使弃答率降低 13.8 个百分点，其中约一半猜测是错的；反事实测试显示把精确病名换成家族名使弃答降低 17.4 个百分点。
正文 · 原文
Abstract:Retrieval-augmented systems often retrieve passages that are accurate and relevant but state the answer at a coarser level of granularity than the question requires. For example, a question may ask which specific disease a drug treats (familial hyperlipidemia), while the retrieved passage mentions the broader disease family (hyperlipidemia). Prior work on context sufficiency treats such coarse passages simply as insufficient. We argue that they form a distinct case, which we call resolvable insufficiency: text alone cannot settle the fine-grained answer, but the broader category it mentions maps to a short list of candidate answers in a domain ontology. When language models read these coarse, family-only passages, they exhibit a phenomenon we call Clade (Category-Level Answering from Deficient Evidence): they answer instead of abstaining or searching further. On biomedical questions from PrimeKG, family-only passages reduce abstention by 13.8 points compared to uninformative passages, and the resulting guesses are wrong about half the time. A counterfactual test confirms the cause: replacing an exact disease name with its broader family name lowers abstention by 17.4 points compared to deleting the sentence. Simple fixes like confidence filtering, scaling to a larger model, and explicitly instructing the model to abstain cannot resolve Clade, and this replicates on ecological species interactions in GloBI. Finally, we test whether an ontology can turn coarse passages into exact answers. Given the correct family, an ontology list includes the true answer in 98.5% of cases, but string matching finds that family on the page for only 19.8%. Letting the model choose the family raises this to 30.0% and end-to-end recovery from 10.0% to 26.8%. Overall, our results show when language models should stop guessing and consult an ontology, and highlight which steps of that process remain unsolved.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.14355 [cs.AI] |
| (or arXiv:2611.14355v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.14355 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: S M Rayeed [view email]
[v1]
Tue, 10 Nov 2026 17:16:06 UTC (1,688 KB)
来源：arXiv cs.AI · arxiv.org