In recent years, Explainable AI (XAI) methods have facilitated profound
validation and knowledge extraction from ML models. While extensively studied
for classification, few XAI solutions have addressed the challenges specific to
regression models. In regression, explanations need to be precisely formulated
to address specific user queries (e.g.\ distinguishing between `Why is the
output above 0?' and `Why is the output above 50?'). They should furthermore
reflect the model's behavior on the relevant data sub-manifold. In this paper,
we introduce XpertAI, a framework that disentangles the prediction strategy
into multiple range-specific sub-strategies and allows the formulation of
precise queries about the model (the `explanandum') as a linear combination of
those sub-strategies. XpertAI is formulated generally to work alongside popular
XAI attribution techniques, based on occlusion, gradient integration, or
reverse propagation. Qualitative and quantitative results, demonstrate the
benefits of our approach.

近年来，可解释性人工智能（Explainable AI，XAI）方法已经促进了机器学习模型的深入验证和知识提取。然而，尽管分类问题得到了广泛研究，但很少有 XAI 解决方案针对回归模型的特定挑战。本文介绍了 XpertAI，一个将预测策略分解为多个范围特定子策略的框架，允许通过这些子策略的线性组合对模型（被解释对象）进行精确查询。XpertAI 通常与基于遮挡、梯度积分或反向传播的流行 XAI 归因技术一起使用，并通过定性和定量结果展示了我们方法的优势。

XpertAI：揭示子流形的模型策略

XpertAI: uncovering model strategies for sub-manifolds

Explainable Artificial Intelligence (XAI) is an emerging field in AI that
aims to address the opaque nature of machine learning models. Furthermore, it
has been shown that XAI can be used to extract input-output relationships,
making them a useful tool in chemistry to understand structure-property
relationships. However, one of the main limitations of XAI methods is that they
are developed for technically oriented users. We propose the XpertAI framework
that integrates XAI methods with large language models (LLMs) accessing
scientific literature to generate accessible natural language explanations of
raw chemical data automatically. We conducted 5 case studies to evaluate the
performance of XpertAI. Our results show that XpertAI combines the strengths of
LLMs and XAI tools in generating specific, scientific, and interpretable
explanations.

提出了 XpertAI 框架，将 XAI 方法与大型语言模型相结合，以访问科学文献，自动生成原始化学数据的易于理解的自然语言解释。通过进行 5 个案例研究来评估 XpertAI 的性能，结果表明 XpertAI 将大型语言模型和 XAI 工具的优势结合起来，生成具体、科学和可解释的解释。