Enhancing ML Model Interpretability: Leveraging Fine-Tuned Large Language Models for Better Understanding of AI
This addresses the need for better explainable AI tools in sectors like battery management, though it is incremental as it combines existing LLM and XAI methods.
The paper tackles the problem of interpreting black-box machine learning models by proposing a reference architecture that uses a fine-tuned large language model in an interactive chatbot, applied to battery state-of-health prediction, and finds it improves interpretability for less experienced users.
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.