Large language models (LLMs) have achieved remarkable success across diverse applications, yet their generic training paradigm limits effectiveness in user-specific scenarios. LLM personalization aims to adapt large models to individual users or user groups by incorporating preferences, histories, and contextual signal...
Rui-Jie Wang, Qing-Kai Zeng, Xuefei Wang et al.· Proceedings of the 32nd ACM...· 0 citations
Cross-domain zero- or few-shot personalization aims to generate user-preferred responses in unseen conversational domains from only a handful of target-domain interactions. Existing adaptation methods struggle to calibrate update magnitude under sparse evidence and thus overfit, whereas history-transfer methods often e...
Xuefei Wang, Jun Han, Zi-Xuan Wang et al.· 0 citations
This work introduces the Counterfactual Quotient Model, which treats action-conditioned futures as equivalent when they differ only by a component shared across actions, and establishes the decision sufficiency, identifiability, common-mode invariance, approximation behavior, and regret properties of the resulting repr...
LLMODE is proposed, a token-efficient framework for irregular spatio-temporal forecasting with a frozen LLM backbone that shows competitive overall performance, with clearer advantages under sparse or dynamically complex irregular sampling.
Di Zhang, Jing-Yang Zhang, Zi-Qian Wang et al.· 0 citations
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