AI RESEARCH
BoolXLLM: LLM-Assisted Explainability for Boolean Models
arXiv CS.AI
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ArXi:2605.12139v1 Announce Type: new 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.