引导大语言模型生成高质量文本分类的反事实解释
Guiding LLMs to Generate High-Fidelity and High-Quality Counterfactual Explanations for Text Classification
AI总结：
本文提出两种分类器引导方法，引导大语言模型生成高质量文本分类的反事实解释，无需微调并提升模型鲁棒性。
摘要：
深度学习中可解释性需求推动了反事实解释的研究，这类解释识别出改变实例的最小变化以改变模型预测。当前反事实（CF）生成方法需要任务特定的微调，并生成低质量文本。尽管大语言模型（LLMs）在高质量文本生成方面有效，但它们在没有微调的情况下难以生成标签翻转的反事实（即改变预测的反事实）。我们引入了两种简单的分类器引导方法，以支持LLMs生成反事实，从而在不进行微调的情况下保持LLMs的优势。尽管方法简单，我们的方法在性能上优于最先进的反事实生成方法，并且在不同LLMs上均有效，突显了通过分类器信息引导LLMs生成反事实的好处。我们进一步表明，通过我们生成的CFs进行数据增强可以提高分类器的鲁棒性。我们的分析揭示了LLMs在反事实生成中的关键问题：LLMs依赖参数知识而不是忠实遵循分类器。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
