根据用户类型自动匹配解释方法，让AI解释更精准可信。
ProfileXAI: User-Adaptive Explainable AI
- 用检索增强的LLM动态选解释器，适配不同用户
- 不同解释器各有所长：LIME最稳，Anchor最简洁，SHAP最受好评
- 支持多类用户，解释质量稳定且可信赖
ProfileXAI是一个模型和领域无关的可解释AI框架，将后验解释器（SHAP、LIME、Anchor）与检索增强的LLM结合，为不同用户生成个性化解释。系统索引多模态知识库，基于量化标准为每条实例选择最优解释器，并通过对话式提示生成有依据的叙述。在心脏病和甲状腺癌数据集上评估了保真度、鲁棒性、简洁性、词元使用量及感知质量。结果表明无单一解释器占优：LIME在保真度-鲁棒性平衡上表现最佳（心脏病数据集上Infidelity ≤0.30，L<0.7）；Anchor生成最稀疏、低词元规则；SHAP获得最高满意度（均值=4.1）。用户画像条件化使词元使用波动≤13%，各类用户评分均保持正向（均值≥3.7，领域专家达3.77），实现高效且可信的解释。
原文摘要 · Abstract (English)
ProfileXAI is a model- and domain-agnostic framework that couples post-hoc explainers (SHAP, LIME, Anchor) with retrieval - augmented LLMs to produce explanations for different types of users. The system indexes a multimodal knowledge base, selects an explainer per instance via quantitative criteria, and generates grounded narratives with chat-enabled prompting. On Heart Disease and Thyroid Cancer datasets, we evaluate fidelity, robustness, parsimony, token use, and perceived quality. No explainer dominates: LIME achieves the best fidelity-robustness trade-off (Infidelity $\le 0.30$, $L<0.7$ on Heart Disease); Anchor yields the sparsest, low-token rules; SHAP attains the highest satisfaction ($\bar{x}=4.1$). Profile conditioning stabilizes tokens ($σ\le 13\%$) and maintains positive ratings across profiles ($\bar{x}\ge 3.7$, with domain experts at $3.77$), enabling efficient and trustworthy explanations.
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