LLM4XAI
Generative Models for Explainable AI Narratives
About the Workshop
Bridging generative AI and explainability research.
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.
We welcome contributions spanning methods, evaluation frameworks, human-subject studies, and position papers that advance our understanding of LLM-generated explanations — see the Call for Papers for topics of interest and submission details.
Key Dates
All deadlines are Anywhere on Earth (AoE) unless noted otherwise.
Organizers
Main point of contact: Gabriele Tolomei & Vittoria Vineis