Active in-context learning for cross-domain entity resolution

Ziheng Zhang, Weixin Zeng, Jiuyang Tang, Hongbin Huang, Xiang Zhao

Abstract: Highlights•We are among the first attempts to explore the ability of large language model (LLM) for performing cross-domain entity resolution tasks, which avoids the extensive fine-tuning efforts required by existing methods.•We devise an active in-context learning strategy and a domain-invariant similarity calculation method to improve the demonstration selection and unleash the power of LLM.•We have thoroughly evaluated our proposal by comparing with other LLM-based and state-of-the-art deep-learning based methods on different datasets, validating the effectiveness of our method.