用大模型将图神经网络的反事实解释转为自然语言，让普通人也能看懂。
Natural Language Counterfactual Explanations for Graphs Using Large Language Models
- 用开源大模型把图模型的反事实样本转成自然语言。
- 在多个图数据集上验证，生成解释准确率高。
- 适合想理解模型决策逻辑的非专业人士使用。
可解释人工智能（XAI）已成为研究深度学习模型内部机理的关键领域。其中，反事实解释因其‘如果……会怎样’的推理方式而备受关注。然而，现有反事实解释常过于技术化，难以被非专家理解。为此，本文利用开源大语言模型，将先进图模型解释器生成的有效反事实实例转化为自然语言描述。在多个图数据集和反事实解释器上的实验表明，该方法能有效生成准确的自然语言解释，关键性能指标表现良好。
原文摘要 · Abstract (English)
Explainable Artificial Intelligence (XAI) has emerged as a critical area of research to unravel the opaque inner logic of (deep) machine learning models. Among the various XAI techniques proposed in the literature, counterfactual explanations stand out as one of the most promising approaches. However, these "what-if" explanations are frequently complex and technical, making them difficult for non-experts to understand and, more broadly, challenging for humans to interpret. To bridge this gap, in this work, we exploit the power of open-source Large Language Models to generate natural language explanations when prompted with valid counterfactual instances produced by state-of-the-art explainers for graph-based models. Experiments across several graph datasets and counterfactual explainers show that our approach effectively produces accurate natural language representations of counterfactual instances, as demonstrated by key performance metrics.
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