用分类器引导大模型生成高质量反事实解释，无需微调。
Guiding LLMs to Generate High-Fidelity and High-Quality Counterfactual Explanations for Text Classification
- 用分类器信息指导大模型生成反事实文本
- 生成的反事实能准确改变预测结果且文本质量高
- 适合需要可解释性与鲁棒性的文本分类应用
深度学习的可解释性需求推动了反事实解释的发展，其旨在找出使模型预测改变的最小输入变动。现有反事实生成方法需针对任务微调，且生成文本质量较低。尽管大语言模型（LLMs）在生成高质量文本方面表现优异，但在无微调情况下难以生成标签翻转型反事实。本文提出两种简单的分类器引导方法，支持LLMs生成反事实，无需微调，同时保留了LLMs的生成优势。实验表明，所提方法优于当前最优反事实生成方法，且适用于多种LLM。进一步发现，利用生成的反事实进行数据增强可提升分类器的鲁棒性。分析揭示：当前LLMs在生成反事实时依赖参数化知识，而非忠实遵循分类器逻辑。
原文摘要 · Abstract (English)
The need for interpretability in deep learning has driven interest in counterfactual explanations, which identify minimal changes to an instance that change a model's prediction. Current counterfactual (CF) generation methods require task-specific fine-tuning and produce low-quality text. Large Language Models (LLMs), though effective for high-quality text generation, struggle with label-flipping counterfactuals (i.e., counterfactuals that change the prediction) without fine-tuning. We introduce two simple classifier-guided approaches to support counterfactual generation by LLMs, eliminating the need for fine-tuning while preserving the strengths of LLMs. Despite their simplicity, our methods outperform state-of-the-art counterfactual generation methods and are effective across different LLMs, highlighting the benefits of guiding counterfactual generation by LLMs with classifier information. We further show that data augmentation by our generated CFs can improve a classifier's robustness. Our analysis reveals a critical issue in counterfactual generation by LLMs: LLMs rely on parametric knowledge rather than faithfully following the classifier.
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