BoolXLLM: LLM-Assisted Explainability for Boolean Models

2026-05-12

AI Summary

This work addresses the challenge non-technical users face in interpreting semantic feature selection and logical rules within Boolean models. To bridge the gap between formal logic and human-readable explanations, the study presents the first end-to-end integration of large language models (LLMs) into the entire Boolean rule learning pipeline. The proposed approach leverages LLMs to support three critical stages: feature selection, recommendation of discretization thresholds for numerical features, and rule compression with natural language explanations. By synergistically combining the symbolic reasoning of BoolXAI classifiers with the language generation capabilities of LLMs, the method maintains competitive predictive performance while substantially enhancing interpretability for non-expert users, effectively reconciling rigorous logical formalism with intuitive, accessible explanations.

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.

Problem

• interpretable machine learning
• Boolean models
• feature selection
• rule interpretation
• explainable AI

Innovation

• Boolean rule learning
• Large Language Models
• Explainable AI
• Feature selection
• Rule interpretation