Entity Resolution (ER) is the problem of semi-automatically determining when two entities refer to the same underlying entity, with applications ranging from healthcare to e-commerce. Traditional ER solutions required considerable manual expertise, including feature engineering, as well as identification and curation of training data. In many instances, such techniques are highly dependent on the domain. With recent advent in large language models (LLMs), there is an opportunity to make ER much more seamless and domain-independent. However, it is also well known that LLMs can pose risks, and that the quality of their outputs can depend on so-called prompt engineering. Unfortunately, a systematic experimental study on the effects of different prompting methods for addressing ER, using LLMs like ChatGPT, has been lacking thus far. This paper aims to address this gap by conducting such a study. Although preliminary in nature, our results show that prompting can significantly affect the quality of ER, although it affects some metrics more than others, and can also be dataset dependent.

翻译：实体解析（Entity Resolution, ER）是指半自动确定两个实体是否指向同一真实实体的问题，其应用范围涵盖医疗健康到电子商务。传统的ER解决方案需要大量人工专业知识，包括特征工程以及训练数据的识别与整理。在许多情况下，此类技术高度依赖于特定领域。随着大语言模型（LLMs）的近期发展，ER有望变得更加无缝且领域无关。然而，众所周知，LLMs存在潜在风险，其输出质量可能依赖于所谓的提示工程（prompt engineering）。遗憾的是，迄今为止，尚未有系统的实验研究探讨使用ChatGPT等LLMs针对ER任务时不同提示方法的效果。本文旨在通过开展此类研究填补这一空白。尽管本研究具有初步性质，但结果显示，提示方法会显著影响ER质量，尽管其对某些指标的影响强于其他指标，且可能依赖于具体数据集。