BoolXLLM: LLM-Assisted Explainability for Boolean Models

• URL: http://arxiv.org/abs/2605.12139v1
• Date: Tue, 12 May 2026 13:58:20 GMT
• Title: BoolXLLM: LLM-Assisted Explainability for Boolean Models
• Abstract summary: Interpretable machine learning aims to provide transparent models whose decision-making processes can be readily understood by humans.<n>Main challenges remain in practice: (i) selecting semantically meaningful features and (ii) translating formal logical rules into accessible explanations.<n>We propose BoolXLLM as a hybrid framework that integrates Large Language Models into the end-to-end pipeline of Boolean rule learning.
• Score: 6.97075461976982
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Interpretable machine learning aims to provide transparent models whose decision-making processes can be readily understood by humans. Recent advances in rule-based approaches, such as expressive Boolean formulas (BoolXAI), offer faithful and compact representations of model behavior. However, for non-technical stakeholders, main challenges remain in practice: (i) selecting semantically meaningful features and (ii) translating formal logical rules into accessible explanations. In this work, we propose BoolXLLM , as a hybrid framework that integrates Large Language Models (LLMs) into the end-to-end pipeline of Boolean rule learning. We augment BoolXAI , an expressive Boolean rule-based classifier, with LLMs at three critical stages: (1) feature selection, where LLMs guide the identification of domain-relevant variables; (2) threshold recommendation, where LLMs propose semantically meaningful discretization strategies for numerical features; and (3) rule compression and interpretation, where Boolean rules are translated into natural language explanations at both global and local levels. This integration bridges formal, faithful explanations with human-understandable narratives. This allows build an explainable AI system that is both theoretically grounded and accessible to non-experts. Early empirical results demonstrate that LLM-assisted pipelines improve interpretability while maintaining competitive predictive performance. Our work highlights the promise of combining symbolic reasoning with language-based models for human-centered explainability.

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