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 13th International LLM4MEM 13 Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2021, Volume 2: KEOD
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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.