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
Effectively modeling the complex and evolving dependencies among multiple variables is a key challenge in multivariate time series anomaly detection (MTSAD). Existing methods typically model channel dependencies in discrete time steps, either window-wise or point-wise. However, they face a granularity dilemma: window-w...
Lijun Sun, Shuai Zhang, Xin Xue et al.· Proceedings of the 32nd ACM...· 0 citations
The "2nd Frontiers in Graph Machine Learning for the Large Model Era (GMLLM'26)" workshop focuses on advancing graph machine learning (GML) techniques in the context of large-scale foundation models. Graphs offer a principled way to represent structured and relational data, making them essential for capturing complex d...
Qing-Yun Sun, Zi-Wei Zhang, Xing-Cheng Fu et al.· Proceedings of the 32nd ACM...· 0 citations
Recent advances enable LLMs to generate simulation code from natural language, yet interpreting 3D physical field outputs remains unsolved. Existing 3D scene compression methods fail on physical fields due to absent semantic grounding and information loss. We discover that typical physical fields exhibit extreme inform...
Chonghan Gao, Haoyi Zhou, Zhemeng Luo et al.· Proceedings of the 32nd ACM...· 0 citations
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