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Zenghua Liao · Jinzhi Liao · Xiang Zhao
Large language models (LLMs) excel when user goals are clearly specified, yet real-world queries are often vague and evolving, forcing LLMs to guess and leading to misaligned responses. Existing approaches attempt to clarify user intents through iterative questioning. While effective in alleviati...
Chao Jiang · Jinzhi Liao · Xiang Zhao
Multimodal knowledge editing (MKE) aims to efficiently rectify outdated or incorrect knowledge in multimodal large language models (MLLMs) while preserving reliability, generality, and locality. Recently, in-context editing (ICE) has emerged as a prevalent paradigm for MKE, focusing on inference-...
Ziyang Chen · Renbing Chen · Daowei Li · Jinzhi Liao · Jiashen Sun · Ke Zeng · Xiang Zhao
Simulating group-level user behavior enables scalable counterfactual evaluation of merchant strategies without costly online experiments. However, building a trustworthy simulator faces two structural challenges. First, information incompleteness causes reasoning-based simulators to over-rational...