MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model
This addresses the challenge of integrating domain knowledge into AI systems for improved interpretability, though it is incremental as it builds on existing rule-based and LLM methods.
The paper tackles the problem of aligning machine learning models with human domain knowledge for trustworthiness and interpretability by proposing MoRE-LLM, which combines a black-box model with LLM-extracted knowledge to enable transparent predictions, achieving competitive performance on tabular datasets compared to interpretable and non-interpretable baselines.
To ensure the trustworthiness and interpretability of AI systems, it is essential to align machine learning models with human domain knowledge. This can be a challenging and time-consuming endeavor that requires close communication between data scientists and domain experts. Recent leaps in the capabilities of Large Language Models (LLMs) can help alleviate this burden. In this paper, we propose a Mixture of Rule Experts guided by a Large Language Model (MoRE-LLM) which combines a data-driven black-box model with knowledge extracted from an LLM to enable domain knowledge-aligned and transparent predictions. While the introduced Mixture of Rule Experts (MoRE) steers the discovery of local rule-based surrogates during training and their utilization for the classification task, the LLM is responsible for enhancing the domain knowledge alignment of the rules by correcting and contextualizing them. Importantly, our method does not rely on access to the LLM during test time and ensures interpretability while not being prone to LLM-based confabulations. We evaluate our method on several tabular data sets and compare its performance with interpretable and non-interpretable baselines. Besides performance, we evaluate our grey-box method with respect to the utilization of interpretable rules. In addition to our quantitative evaluation, we shed light on how the LLM can provide additional context to strengthen the comprehensibility and trustworthiness of the model's reasoning process.