使用大型语言模型生成图的自然语言反事实解释
Natural Language Counterfactual Explanations for Graphs Using Large Language Models
AI总结：
本研究利用开源大型语言模型，将图模型解释器生成的反事实实例转化为自然语言解释，实验证明该方法能有效生成准确且易于理解的解释。
摘要：
可解释人工智能（XAI）已成为揭示（深度）机器学习模型不透明内部逻辑的关键研究领域。文献中提出的各种XAI技术中，反事实解释是最有前途的方法之一。然而，这些“假设”解释通常复杂且技术性强，使得非专家难以理解，更广泛地说，人类也难以解释。为了弥合这一差距，在本研究中，我们利用开源大型语言模型的强大能力，当输入最先进的图模型解释器生成的有效反事实实例作为提示时，生成自然语言解释。在多个图数据集和反事实解释器上的实验表明，正如关键性能指标所证明的那样，我们的方法有效地生成了准确的自然语言反事实实例表示。
英文摘要：
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.
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。
