Xiang Zhao

Entity Matching (EM) is a fundamental task in data integration, traditionally studied over structured data such as tables and knowledge graphs. In modern repositories, real-world objects are often represented across multiple modalities, including \emph{structured entities} with symbolic attribute...

Entity matching is a fundamental task in a wide range of retrieval and knowledge applications, aiming to identify whether two objects correspond to the same real-world entity across heterogeneous sources. Typical variants include entity resolution (ER), entity linking (EL), and entity alignment (...

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...

Multimodal knowledge editing (MKE) aims to efficiently rectify outdated or incorrect knowledge in multimodal large language models (MLLMs) while preserving reliability, generality, and locality.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-...

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...