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Paper Fine-Tunes LLM Recommender to Explain Its Suggestions Safely

A new arXiv preprint proposes treating safety as a constraint when fine-tuning a large language model used as a recommender system. Standard recommenders are trained only to predict the next item a user will engage with, not to justify the prediction, so the authors add self-explanation as a training objective. The goal is to give users personalized reasons for suggestions without letting the generated explanations violate safety requirements.

recommender-systems/fine-tuning/safety/explainability