Label Annotation for Tabular Anomaly Detection with Large Language Models

Abstract

Tabular Anomaly Detection (TAD) plays a fundamental role in securing real-world applications. Despite rapid advances in TAD, the prohibitive cost of human-centric label annotation remains a primary bottleneck for large-scale production systems. To alleviate this bottleneck, we propose a novel ''coarse-to-fine'' label annotation pipeline to improve labor efficiency through a coarse-grained label annotation and fine-grained human verification. Specifically, Large Language Models (LLMs), with their strong cross-domain capabilities, serve as a promising solution for the coarse-grained annotation stage. However, effectively generalizing LLMs to coarse-grained annotation remains challenging due to the inability to ground semantic priors in rigorous deduction, as well as the overfitting risks inherent in single-domain fine-tuning. Accordingly, we introduce TaDGeneral, a large-scale cross-domain corpus constructed by fusing deductive reasoning paths from diverse domains. This design bridges the reasoning gap while preventing the memorization of local shortcuts. Building upon this, we develop TaDFM, a foundation model tailored to internalize generalizable deductive logic for effective zero-shot annotation. Extensive experiments on both public and large-scale real-world TAD datasets demonstrate the superiority of TaDFM over representative methods, with its practical value further validated by an industrial case study. Code: https://github.com/cshhzhao/TaDFM.