June 6, 2024, 4:44 a.m. | Yuchen Zhuang, Haotian Sun, Yue Yu, Qifan Wang, Chao Zhang, Bo Dai

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02888v1 Announce Type: cross
Abstract: Personalization has emerged as a critical research area in modern intelligent systems, focusing on mining users' behavioral history and adapting to their preferences for delivering tailored experiences. Despite the remarkable few-shot capabilities exhibited by black-box large language models (LLMs), the inherent opacity of their model parameters presents significant challenges in aligning the generated output with individual expectations. Existing solutions have primarily focused on prompt design to incorporate user-specific profiles and behaviors; however, such approaches often …

arxiv box cs.ai cs.cl cs.lg factorization framework hydra llm personalization type

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