BoolXLLM: LLM-Assisted Explainability for Boolean Models
A new hybrid framework called BoolXLLM integrates Large Language Models into the Boolean rule learning pipeline to improve interpretability. The system augments the BoolXAI classifier at three stages: feature selection, threshold recommendation, and rule translation. LLMs guide domain-relevant variable identification and propose meaningful discretization strategies for numerical features. The goal is to make formal logical rules accessible to non-technical stakeholders. The research is published on arXiv under identifier 2605.12139.
Key facts
- BoolXLLM is a hybrid framework integrating LLMs into Boolean rule learning.
- It augments the BoolXAI classifier at three stages: feature selection, threshold recommendation, and rule translation.
- LLMs guide identification of domain-relevant variables.
- LLMs propose semantically meaningful discretization strategies for numerical features.
- The work aims to make formal logical rules accessible to non-technical stakeholders.
- Published on arXiv with identifier 2605.12139.
- The paper is classified as a new announcement type.
- Interpretable machine learning seeks transparent models understandable by humans.
Entities
Institutions
- arXiv