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 2018 International Conference on Management of Data, SIGMOD Confer- ence 2018, Houston, TX, USA, June 10-15, 2018
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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.