Entity resolution (ER) is an important data integration task with a wide spectrum of applications. The state-of-the-art solutions on ER rely on pre-trained language models (PLMs), which require fine-tuning on a lot of labeled matching/non-matching entity pairs. Recently, large languages models (LLMs), such as GPT-4, have shown the ability to perform many tasks without tuning model parameters, which is known as in-context learning (ICL) that facilitates effective learning from a few labeled input context demonstrations. However, existing ICL approaches to ER typically necessitate providing a task description and a set of demonstrations for each entity pair and thus have limitations on the monetary cost of interfacing LLMs. To address the problem, in this paper, we provide a comprehensive study to investigate how to develop a cost-effective batch prompting approach to ER. We introduce a framework BATCHER consisting of demonstration selection and question batching and explore different design choices that support batch prompting for ER. We also devise a covering-based demonstration selection strategy that achieves an effective balance between matching accuracy and monetary cost. We conduct a thorough evaluation to explore the design space and evaluate our proposed strategies. Through extensive experiments, we find that batch prompting is very cost-effective for ER, compared with not only PLM-based methods fine-tuned with extensive labeled data but also LLM-based methods with manually designed prompting. We also provide guidance for selecting appropriate design choices for batch prompting.

翻译：实体解析（ER）是一项重要的数据集成任务，具有广泛的应用场景。当前最先进的ER解决方案依赖于预训练语言模型（PLM），这些模型需要大量标注的匹配/非匹配实体对进行微调。近年来，大规模语言模型（LLM）如GPT-4展现出在不调整模型参数的情况下执行多项任务的能力，这被称为上下文学习（ICL），它通过少量标注的输入上下文示例实现高效学习。然而，现有的基于ICL的ER方法通常需要为每个实体对提供任务描述和一组示例，从而在调用LLM的货币成本上存在局限。为解决这一问题，本文全面研究了如何开发一种面向ER的高性价比批提示方法。我们提出了BATCHER框架，包含示例选择与问题批处理，并探索了支持ER批提示的不同设计选项。同时，我们设计了一种基于覆盖的示例选择策略，在匹配准确率与货币成本之间实现了有效平衡。我们通过深入评估探索了设计空间，并评价了所提策略。大量实验表明，与基于PLM且需大量标注数据微调的方法以及基于LLM且需人工设计提示的方法相比，批提示在ER中具有极高的性价比。此外，我们还为选择适当的批提示设计选项提供了指导。