LLM4XAI: Generative Models for Explainable AI Narratives

Abstract

As AI systems are increasingly deployed in high-stakes domains, regulatory and societal demands for transparency and interpretability continue to grow. While classical Explainable AI (XAI) methods produce structured technical artifacts (e.g., feature attributions and counterfactual examples), these remain difficult to interpret for non-technical users and only partially meet usability and legal requirements. Large Language Models (LLMs) can transform these structured outputs into accessible natural-language XAI narratives, but they introduce new challenges regarding faithfulness, hallucination, and epistemic misalignment. LLM4XAI sits at the intersection of XAI, NLP, IR, and HCI, investigating how generative systems can act as reliable mediators. The workshop provides a multidisciplinary forum for both academia and industry, focusing on the grounding, robustness, and real-world deployment of XAI narratives to ensure AI explanations bridge the gap between technical rigor and end-user accessibility.