arXiv cs.CL· Bo Qu, Zhurong Wang, Frank Zahradnik, Ming Ouyang·· 11 小时前AI 评分29
为盲区打补丁：面向冻结欺诈检测器的可逆 LLM 规则叠加层
Patching the Blind Spots: Reversible LLM Rule Overlay for Frozen Fraud Detectors
AI 导读
研究提出一种不修改已部署模型的可逆 LLM 规则叠加框架，由 LLM 针对 SHAP 显示模型低估的特征生成规则，经筛选校准后以可加、可逆的 log-lift 叠加层在生产阈值上运行。
正文 · 原文
Abstract:In industrial tabular fraud classification, gradient-boosted trees remain state-of-the-art, but even a well-trained model has blind spots, and retraining the deployed model is not always safe. We propose a framework that patches such a model without modifying it: LLMs propose rules anchored on features the model underweights by SHAP, and the resulting noisy pool is screened for empirical support and calibrated into an additive, reversible log-lift overlay that runs at the production cut-off. Single-LLM benefit is high-variance and inverts between datasets, so we pool three diverse backbones by equal-weight logit averaging, trading peak gain for a bounded worst case. On a real-world e-commerce fraud detector trained on selection-biased data, continued training regresses even when carefully tuned, whereas the overlay gains on average +2.26% recall at equal precision ($\Delta$R@P) and +1.60% precision at equal recall ($\Delta$P@R) over production cut-offs spanning 30-57% precision and 23-63% recall. On a public benchmark of seven designed bias-shift variants it raises $\Delta$P@R on every variant and keeps $\Delta$R@P near neutral. Each amendment is a readable predicate naming the fraud pattern it covers.
| Comments: | 11 pages, 1 figure, 7 tables |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2611.08714 [cs.LG] |
| (or arXiv:2611.08714v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2611.08714 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Bo Qu [view email]
[v1]
Thu, 5 Nov 2026 21:12:11 UTC (35 KB)
来源：arXiv cs.CL · arxiv.org