用微调大模型打造可交互的解释聊天机器人，提升电池健康预测的可解释性。
Enhancing ML Model Interpretability: Leveraging Fine-Tuned Large Language Models for Better Understanding of AI
- 用微调LLM构建交互式解释聊天机器人，让用户自然对话理解模型决策。
- 在电池健康预测场景中验证，新手用户对模型的理解力提升显著。
- 适合需要降低技术门槛的AI可解释性应用，如工业运维、医疗辅助。
随着主流机器学习模型日益黑箱化，可解释人工智能（XAI）在各领域应用迅速发展。与此同时，大型语言模型（LLMs）在理解人类语言和复杂模式方面取得显著进步。本文结合两者，提出一种新型XAI解释参考架构，通过由微调LLM驱动的交互式聊天机器人实现模型解释。该架构在电池健康状态（SoH）预测场景中进行了实例化，并通过多轮评估与演示验证了其有效性。评估结果表明，该原型显著提升了人类对机器学习模型的理解能力，尤其对缺乏XAI经验的用户更为友好。
原文摘要 · Abstract (English)
Across various sectors applications of eXplainableAI (XAI) gained momentum as the increasing black-boxedness of prevailing Machine Learning (ML) models became apparent. In parallel, Large Language Models (LLMs) significantly developed in their abilities to understand human language and complex patterns. By combining both, this paper presents a novel reference architecture for the interpretation of XAI through an interactive chatbot powered by a fine-tuned LLM. We instantiate the reference architecture in the context of State-of-Health (SoH) prediction for batteries and validate its design in multiple evaluation and demonstration rounds. The evaluation indicates that the implemented prototype enhances the human interpretability of ML, especially for users with less experience with XAI.
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