AI Research
COPE: Continual Personalization of LLMs under Sparse User Feedback via User Embeddings and Self-Evaluation
Bridging the gap between universal alignment and individualized AI personalization through adaptive user embeddings.
While Large Language Models (LLMs) have achieved remarkable results across various benchmarks, their alignment with normative values often results in homogenized responses that fail to address diverse user preferences. Existing training-free methods often occupy valuable context ...
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ArXiv CS.LG
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ArXiv CS.LG
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COPE enables LLMs to adapt to individual user preferences in real-time without the overhead of massive fine-tuning or context-window limitations. This advancement signals a shift toward truly personalized AI agents that evolve alongside their users through sparse, natural feedback loops.
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