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: The Semantic Web – ISWC 2021: 20th International Semantic Web Con- ference, ISWC 2021, Virtual Event, October 24–28, 2021, Proceedings
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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.