一个以数据为中心的HitL框架,用于使用可解释AI进行NLP数据集的系统错误分析.
Ahmed El-Sayed1, Aly Nasr2, Youssef Mohamed2
1Computer and Systems Engineering Department, Faculty of Engineering, Alexandria University, Alexandria, Egypt. ahmed_elsayed@alexu.edu.eg.
Scientific reports
|August 19, 2025
概括
数据中心的人工智能通过系统错误分析来改进NLP数据集. 使用可解释AI的X-Deep框架,识别和减轻阿拉伯情绪检测中的数据问题.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 人工智能 (AI) 是一种人工智能.
背景情况:
- 数据中心的AI在整个模型生命周期中代地提高了数据质量,与传统的以模型为中心的AI不同.
- 数据中心人工智能的应用和好处,特别是错误分析,在NLP数据集中仍未得到充分探索.
研究的目的:
- 在以数据为中心的AI框架内调查NLP错误分析的表现.
- 提出和评估X-Deep框架用于使用可解释AI (XAI) 调试NLP数据集.
主要方法:
- 开发了X-Deep,这是一个Human-in-the-Loop框架,集成XAI技术 (LIME,SHAP) 来进行NLP数据集调试.
- 进行了阿拉伯情绪检测的案例研究,分析了四个分类器 (Naive Bayes,物流回归,GRU,MARBERT) 的错误分类实例.
主要成果:
- 确定了关键数据异常,包括虚假的相关性,偏见模式以及阿拉伯情绪检测数据集中的其他不规则.
- 在X-Deep框架内证明了XAI技术在发现细微数据问题的有效性.
结论:
- 通过X-Deep进行系统错误分析对于提高NLP数据集质量和模型性能至关重要.
- 这些发现为阿拉伯语情感检测和其他NLP任务中增强数据增强策略提供了基础.
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