对比五种文本分类器反事实解释方法，发现传统方法有效但新模型更自然。
A Comparative Analysis of Counterfactual Explanation Methods for Text Classifiers
- 用替换法和大模型生成反事实文本，比较其效果。
- 传统方法改变分类结果成功率高，大模型生成更自然但常无效。
- 建议融合梯度法与大模型优势，生成既真实又有效的解释。
反事实解释可通过生成轻微修改的文本输入来解释和调试文本分类器，从而改变分类器的输出。本文在两个数据集上，对BERT文本分类器采用五种方法生成反事实解释，并使用三个评估指标进行评测。实验结果表明，成熟的基于白盒替换的方法能有效生成可改变分类器输出的有效反事实文本；而基于大语言模型（LLMs）的新方法虽在生成自然、语言上合理的文本方面表现优异，但常无法生成真正改变分类结果的有效反事实。基于此，我们建议开发融合梯度法与大模型技术的新方法，以生成高质量、有效且语言自然的反事实解释。
原文摘要 · Abstract (English)
Counterfactual explanations can be used to interpret and debug text classifiers by producing minimally altered text inputs that change a classifier's output. In this work, we evaluate five methods for generating counterfactual explanations for a BERT text classifier on two datasets using three evaluation metrics. The results of our experiments suggest that established white-box substitution-based methods are effective at generating valid counterfactuals that change the classifier's output. In contrast, newer methods based on large language models (LLMs) excel at producing natural and linguistically plausible text counterfactuals but often fail to generate valid counterfactuals that alter the classifier's output. Based on these results, we recommend developing new counterfactual explanation methods that combine the strengths of established gradient-based approaches and newer LLM-based techniques to generate high-quality, valid, and plausible text counterfactual explanations.
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