可解释性助手：用于解读能耗模型的可对话XAI界面
Explainability Assistant: A Conversational XAI Interface for Interpreting Energy Consumption Models
- Nupp Software
- Apintech Ltd（Apintech有限公司）
- POLIS-21 Group（POLIS-21集团）
- Hellenic Mediterranean University（希腊地中海大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对能耗预测模型难以解读的问题，提出基于大语言模型函数调用的可对话XAI系统，实现94%意图解析准确率，专家评估显示其优于传统仪表板。
AI中文摘要：
能耗预测依赖于日益复杂的机器学习（ML）模型，例如基于遗传编程的符号回归器，其预测结果对于设施管理者和建筑运营商而言可能难以解读。可解释人工智能（XAI）技术旨在解决这种不透明性，但传统的XAI仪表板需要大量的技术专业知识，并且对于动态、情境感知的查询所提供的灵活性有限。可对话XAI系统提供了一种有前景的替代方案；然而，以往的方法，如TalkToModel，受限于僵化的自定义语法，其意图解析准确率仅为76.8%。本文介绍了可解释性助手（Explainability Assistant），一个开源的、可对话的XAI系统，该系统利用现代大语言模型（LLMs）的函数调用能力来克服这些局限。该系统实现了94%的意图解析准确率，支持灵活的自然语言交互，并且无需针对特定任务进行微调即可适应不同的机器学习问题类型。我们展示了该系统的架构，并报告了一项与能源领域专家进行的对比评估结果，该评估将可解释性助手与传统XAI仪表板进行了对比。评估结果表明，该系统的可用性有所提升，任务准确率保持一致，所有专家一致倾向于在实际使用中采用可对话界面。
英文摘要：
Energy consumption forecasting relies on increasingly complex machine learning (ML) models, such as Genetic Programming-based symbolic regressors, whose predictions can be difficult for facility managers and building operators to interpret. Explainable Artificial Intelligence (XAI) techniques address this opacity, but traditional XAI dashboards require substantial technical expertise and provide limited flexibility for dynamic, context-aware inquiry. Conversational XAI systems offer a promising alternative; however, previous approaches, such as TalkToModel, were constrained by rigid custom grammars and achieved only 76.8% intent-parsing accuracy. This paper introduces the Explainability Assistant, an open-source conversational XAI system that leverages the function-calling capabilities of modern Large Language Models (LLMs) to overcome these limitations. The system achieves 94% intent-parsing accuracy, supports flexible natural language interaction, and adapts to different ML problem types without task-specific fine-tuning. We present the system's architecture and report results from a comparative evaluation conducted with energy domain specialists, contrasting the Explainability Assistant with a traditional XAI dashboard. The evaluation suggests improved usability and consistent task accuracy, with all experts unanimously preferring the conversational interface for practical use.
