COPE: Continual Personalization of LLMs under Sparse User Feedback via User Embeddings and Self-Evaluation
Experiments show that COPE consistently outperforms strong training-free and training-based baselines under sparse feedback, and remains complementary to Retrieval-Augmented Prompting, and further analyses confirm COPE's reliable self-evaluation, meaningful preference patterns, stable general capabilities, and robustne...
Rui-Ke Cao, Fu-Gen Yao, Liang Dong et al.
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