<ul><li>This news discusses the research paper titled 'Personalized Language Generation via Bayesian Metric Augmented Retrieval'.</li><li>The paper proposes a method for personalized language generation by optimizing the style and level of explanation based on the user's preferences.</li><li>The method involves three phases: initial model learning using DPP, personalized model usage and learning with Bayesian metrics, and personalized model utilization for search and inference.</li><li>The results show improved search accuracy, higher generation quality, and dynamic adaptability to user preferences.</li></ul>

Personalized Language Generation via Bayesian Metric Augmented Retrieval

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