Entity Matching is the task of deciding if two entity descriptions refer to the same real-world entity. State-of-the-art entity matching methods often rely on fine-tuning Transformer models such as BERT or RoBERTa. Two major drawbacks of using these models for entity matching are that (i) the models require significant amounts of fine-tuning data for reaching a good performance and (ii) the fine-tuned models are not robust concerning out-of-distribution entities. In this paper, we investigate using ChatGPT for entity matching as a more robust, training data-efficient alternative to traditional Transformer models. We perform experiments along three dimensions: (i) general prompt design, (ii) in-context learning, and (iii) provision of higher-level matching knowledge. We show that ChatGPT is competitive with a fine-tuned RoBERTa model, reaching an average zero-shot performance of 83% F1 on a challenging matching task on which RoBERTa requires 2000 training examples for reaching a similar performance. Adding in-context demonstrations to the prompts further improves the F1 by up to 5% even using only a small set of 20 handpicked examples. Finally, we show that guiding the zero-shot model by stating higher-level matching rules leads to similar gains as providing in-context examples.

翻译：实体匹配是指判断两个实体描述是否指向同一现实世界实体的任务。最先进的实体匹配方法通常依赖于微调BERT或RoBERTa等Transformer模型。使用这些模型进行实体匹配的两个主要缺点是：（i）模型需要大量微调数据才能达到良好性能；（ii）微调模型对分布外实体缺乏鲁棒性。在本文中，我们研究了使用ChatGPT进行实体匹配的方法，将其作为传统Transformer模型的一种更鲁棒、训练数据效率更高的替代方案。我们从三个维度进行了实验：（i）通用提示设计、（ii）上下文学习以及（iii）提供更高层次的匹配知识。实验表明，ChatGPT与微调后的RoBERTa模型具有竞争力，在一项具有挑战性的匹配任务上，其平均零样本性能达到83%的F1值，而RoBERTa需要2000个训练样本才能达到类似性能。即使仅使用20个人工精选示例，在提示中添加上下文演示也进一步将F1值提升了多达5%。最后，我们证明通过陈述更高层次的匹配规则来指导零样本模型，能带来与提供上下文示例相似的性能提升。