<ul><li>The paper introduces a method called Mixture of Rule Experts guided by a Large Language Model (MoRE-LLM) to align machine learning models with human domain knowledge.</li><li>MoRE-LLM combines a data-driven black-box model with knowledge extracted from a Large Language Model (LLM) to enable domain knowledge-aligned and transparent predictions.</li><li>The Mixture of Rule Experts (MoRE) generates local rule-based surrogates during training and utilizes them for the classification task, while the LLM enhances domain knowledge alignment and provides context to the rules.</li><li>The proposed method ensures interpretability and is evaluated on various datasets, comparing its performance with interpretable and non-interpretable baselines.</li></ul>

MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model

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