Ensembling LLM-Induced Decision Trees for Explainable and Robust Error Detection

作者：Mengqi Wang, Jianwei Wang, Qing Liu, Xiwei Xu, Zhenchang Xing, Liming Zhu, Michael Bain, Wenjie Zhang · 年份：2026 · DOI：10.1145/3770855.3817642 · 研究领域：Computer science、Machine learning、Data mining、Artificial intelligence

Error detection (ED), which aims to identify incorrect or inconsistent cell values in tabular data, is important for ensuring data quality. Recent state-of-the-art ED methods leverage the pre-trained knowledge and semantic capability embedded in large language models (LLMs) to directly label whether a cell is erroneous. However, this LLM-as-a-labeler pipeline (1) outputs raw detection results through an implicit, black-box process that lacks traceability, offering little explicit justification for why a particular prediction is made, and (2) relies on a single-pass inference that is inherently susceptible to model stochasticity, resulting in inconsistent detections and a lack of robustness across varying contexts. To address these limitations, we propose an LLM-as-an-inducer framework that uses an LLM to induce a decision tree for ED (termed TreeED) and further ensembles multiple such trees for consensus detection (termed ForestED), thereby improving explainability and robustness. Specifically, based on prompts derived from data context, decision tree specifications and output requirements, TreeED queries the LLM to induce the decision tree skeleton, whose root-to-leaf decision paths specify the stepwise procedure for evaluating a given sample. Each tree contains three types of nodes: (1) rule nodes that perform simple validation checks (e.g., format or range), (2) Graph Neural Network (GNN) nodes that capture complex patterns (e.g., functional dependencies), and (3) leaf nod...