用LIME解释NLP模型决策依据，提升可理解性。
Explainability in Neural Networks for Natural Language Processing Tasks
- 用LIME分析文本分类中MLP模型的特征贡献
- 局部解释有效但难以捕捉全局特征关系
- 适合关注模型透明度的研究者与从业者
神经网络常被视为黑箱模型，尤其在自然语言处理应用中，其内部机制难以理解。为应对这一问题，局部可解释模型无关解释（LIME）等解释技术应运而生，成为揭示复杂系统行为的重要工具。本研究利用LIME解释一个在文本分类任务上训练的多层感知机（MLP）神经网络。通过分析各特征对预测结果的贡献，LIME提升了模型的可解释性，支持更明智的决策。尽管在提供局部解释方面表现良好，但LIME在捕捉全局模式和特征交互方面仍存在局限。本文总结了LIME的优势与不足，并指出了未来实现更全面可解释性的方向。
原文摘要 · Abstract (English)
Neural networks are widely regarded as black-box models, creating significant challenges in understanding their inner workings, especially in natural language processing (NLP) applications. To address this opacity, model explanation techniques like Local Interpretable Model-Agnostic Explanations (LIME) have emerged as essential tools for providing insights into the behavior of these complex systems. This study leverages LIME to interpret a multi-layer perceptron (MLP) neural network trained on a text classification task. By analyzing the contribution of individual features to model predictions, the LIME approach enhances interpretability and supports informed decision-making. Despite its effectiveness in offering localized explanations, LIME has limitations in capturing global patterns and feature interactions. This research highlights the strengths and shortcomings of LIME and proposes directions for future work to achieve more comprehensive interpretability in neural NLP models.
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