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Vittoria Vineis

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#generative ai Book Open access Nov 2026

LLM4XAI: Generative Models for Explainable AI Narratives

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...

Gabriele Tolomei, Zi-Heng Chen, Flavio Giorgi et al. · Proceedings of the 35th ACM... · 0 citations

#artificial intelligence Preprint Sep 2026

Explaining AI Agents Through Execution Traces

This work presents a post-hoc XAI framework that transforms a lengthy agent's execution trace into a structured report and a faithful natural-language explanation explicitly grounded in its observable behavior, outperforming naive LLM-generated explanations.

Vittoria Vineis, Fabiano Veglianti, Lorenzo Antonelli et al. · 0 citations