AI RESEARCH
Rethinking Personalization in Large Language Models at the Token Level
arXiv CS.CL
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ArXi:2603.06595v1 Announce Type: new With large language models (LLMs) now performing strongly across diverse tasks, there is growing demand for them to personalize outputs for individual users. Personalization is typically framed as an additional layer on top of a base NLP task, requiring model responses to meet user-specific needs while still accomplishing the underlying task. From a token-level perspective, different tokens in a response contribute to personalization to varying degrees.