LLM4MEM achieves an average 5.1% F1 improvement on six multi-table entity matching datasets by combining prompt-based attribute coordination, transitive embedding matching, and density-aware pruning.
In: Proceedings of the 31st International Conference on Computational Linguistics, COLING 2025, Abu Dhabi, UAE, January 19-24
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Unlocking the Power of Large Language Models for Multi-table Entity Matching
LLM4MEM achieves an average 5.1% F1 improvement on six multi-table entity matching datasets by combining prompt-based attribute coordination, transitive embedding matching, and density-aware pruning.