AI RESEARCH
Unlocking the Power of Large Language Models for Multi-table Entity Matching
arXiv CS.CL
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ArXi:2604.21238v1 Announce Type: new Multi-table entity matching (MEM) addresses the limitations of dual-table approaches by enabling simultaneous identification of equivalent entities across multiple data sources without unique identifiers. However, existing methods relying on pre-trained language models struggle to handle semantic inconsistencies caused by numerical attribute variations. Inspired by the powerful language understanding capabilities of large language models (LLMs), we propose a novel LLM-based framework for multi-table entity matching, termed LLM4