High-quality training data is essential for the success of machine learning models. However, real-world datasets often contain mixed types of errors arising from systematic flaws in data preparation pipelines, including label errors, feature errors, and spurious correlations. Effective debugging of training data requires both detecting erroneous samples and identifying their specific error types to enable targeted repair, yet existing data cleaning and attribution methods fail to adequately address this dual requirement. In this paper, we propose DeMix, a novel framework that simultaneously diagnoses erroneous samples and their error types. Our key insight is that different error types produce distinct patterns on model behavior. DeMix captures such error-specific patterns by influence vectors that characterize how each training sample affects model predictions across all validation samples. We formulate training data debugging as a multi-label classification problem where a classifier is developed to predict error types directly from influence vectors. We further introduce an intervention-based learning strategy that guides the classifier to capture invariant rationales specific to each error type, ensuring the learned classifier generalizes effectively. Empirical evaluations on 11 tasks across tabular data prediction, recommendation systems, and LLM alignment demonstrate that DeMix significantly outperforms state-of-the-art approaches, achieving a 22.61% improvement in data debugging F1-score and a 9.32% gain in task model performance after data repair. Code is available at: https://github.com/SJTU-DMTai/DeMix.

翻译：摘要：高质量训练数据是机器学习模型成功的关键。然而，现实数据集往往包含由数据准备流程系统性缺陷引发的混合类型错误，包括标签错误、特征错误和虚假关联。有效的训练数据调试既需要检测异常样本，又需要识别其具体错误类型以实现针对性修复，但现有数据清洗与归因方法未能充分满足这一双重需求。本文提出DeMix框架，能够同时诊断异常样本及其错误类型。我们的核心洞察在于：不同错误类型会在模型行为中产生显著模式差异。DeMix通过影响向量捕捉此类错误特异性模式——该向量表征每个训练样本如何影响模型对所有验证样本的预测结果。我们将训练数据调试建模为多标签分类问题，开发分类器直接从影响向量预测错误类型。进一步引入基于干预的学习策略，引导分类器捕获每种错误类型特有的不变因果机制，确保分类器具备良好的泛化能力。在表格数据预测、推荐系统和大语言模型对齐等11类任务上的实验表明，DeMix显著超越现有最优方法：数据调试F1分数提升22.61%，数据修复后任务模型性能提升9.32%。代码开源地址：https://github.com/SJTU-DMTai/DeMix。