most citedUnderstanding Risk and Dependency in AI Chatbot Use from User Discourse

2 citations · 5 across the 15 of their papers we have counts for

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cs.AI2026

Aplaud: Adaptive Personalized Low-Rank Decomposition for User-Specific LLM

Xinyu Li, Ruoming Jin, Jianfeng Zhu +2

In this paper, we study the problem of personalized survey response prediction using fine-tuned large language models (LLMs). This task poses unique challenges: limited per-user tr…

cs.AI2026

PLUME: Parameter-Efficient Personalization of Large Language Models via Low-Rank User Modulation in Shared Subspaces

Xinyu Li, Hao Zhou, Jianfeng Zhu +4

Personalizing large language models (LLMs) is essential for delivering AI assistance that aligns with individual users' styles, intents, and preferences. While per-user fine-tuning…