用原型网络让大模型分类结果可解释，同时保持高准确率。
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks
- 用原型网络捕捉文本语义特征，实现基于实例的解释。
- 在讽刺检测任务中提升准确率，且支持逐样本解释。
- 适合需要透明决策过程的NLP应用，如医疗、金融领域。
预训练的基于Transformer的语言模型在自然语言处理任务中表现出色，但其黑箱特性导致可解释性差，成为一大挑战。本文致力于在使用语言模型作为编码器的同时，构建内在可解释的文本分类模型，通过原型网络实现。研究首先针对讽刺检测任务，提出一种捕捉情感矛盾的方法，在提升准确率的同时提供逐样本的分类解释。随后，设计了一种新型白盒多头图注意力原型网络，可在不损失原始黑箱模型精度的前提下解释分类决策。此外，正探索结合对比学习重构可解释图神经网络，以进一步提升文档分类任务中的可解释性与性能。
原文摘要 · Abstract (English)
Pretrained transformer-based Language Models (LMs) are well-known for their ability to achieve significant improvement on NLP tasks, but their black-box nature, which leads to a lack of interpretability, has been a major concern. My dissertation focuses on developing intrinsically interpretable models when using LMs as encoders while maintaining their superior performance via prototypical networks. I initiated my research by investigating enhancements in performance for interpretable models of sarcasm detection. My proposed approach focuses on capturing sentiment incongruity to enhance accuracy while offering instance-based explanations for the classification decisions. Later, I developed a novel white-box multi-head graph attention-based prototype network designed to explain the decisions of text classification models without sacrificing the accuracy of the original black-box LMs. In addition, I am working on extending the attention-based prototype network with contrastive learning to redesign an interpretable graph neural network, aiming to enhance both the interpretability and performance of the model in document classification.
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