Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration

• URL: http://arxiv.org/abs/2312.03987v1
• Date: Thu, 7 Dec 2023 02:09:27 GMT
• Title: Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration
• Authors: Meihao Fan and Xiaoyue Han and Ju Fan and Chengliang Chai and Nan Tang and Guoliang Li and Xiaoyong Du
• Abstract summary: We provide a comprehensive study to investigate how to develop a cost-effective batch prompting approach to ER. We find that batch prompting is very cost-effective for ER, compared with PLM-based methods fine-tuned with extensive labeled data. We also devise a covering-based demonstration selection strategy that achieves an effective balance between matching accuracy and monetary cost.
• Score: 26.65259285701739
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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