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cs.LG2026
COPE: Continual Personalization of LLMs under Sparse User Feedback via User Embeddings and Self-Evaluation
Ruike Cao, Fugen Yao, Liang Dong +3
While Large Language Models (LLMs) have achieved remarkable results across various benchmarks, their alignment with normative values often results in homogenized responses that fai…
cs.LG2025
Investigating the Sensitivity of Pre-trained Audio Embeddings to Common Effects
Victor Deng, Changhong Wang, Gael Richard +1
In recent years, foundation models have significantly advanced data-driven systems across various domains. Yet, their underlying properties, especially when functioning as feature…