PONTE: Personalized Orchestration for Natural Language Trustworthy Explanations
Explainable Artificial Intelligence (XAI) seeks to enhance the transparency and accountability of machine learning systems, yet most methods follow a one-size-fits-all paradigm that neglects user differences in expertise, goals, and cognitive needs. Although Large Language Models can translate technical explanations into natural language, they introduce challenges related to faithfulness and hallucinations. To address these challenges, we present PONTE (Personalized Orchestration for Natural language Trustworthy Explanations), a human-in-the-loop framework for adaptive and reliable XAI narratives. PONTE models personalization as a closed-loop validation and adaptation process rather than ...
| Authors | Vittoria Vineis, Matteo Silvestri, Lorenzo Antonelli et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 March 2026 |
| Last updated at source | 6 March 2026 |
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