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APrompt4EM: Augmented Prompt Tuning for Generalized Entity Matching
May 9, 2024, 4:47 a.m. | Yikuan Xia, Jiazun Chen, Xinchi Li, Jun Gao
cs.CL updates on arXiv.org arxiv.org
Abstract: Generalized Entity Matching (GEM), which aims at judging whether two records represented in different formats refer to the same real-world entity, is an essential task in data management. The prompt tuning paradigm for pre-trained language models (PLMs), including the recent PromptEM model, effectively addresses the challenges of low-resource GEM in practical applications, offering a robust solution when labeled data is scarce. However, existing prompt tuning models for GEM face the challenges of prompt design and …
abstract arxiv challenges cs.ai cs.cl data data management generalized language language models management paradigm prompt prompt tuning records the prompt type world
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