Entity resolution, the task of identifying and consolidating records that
pertain to the same real-world entity, plays a pivotal role in various sectors
such as e-commerce, healthcare, and law enforcement. The emergence of Large
Language Models (LLMs) like GPT-4 has introduced a new dimension to this task,
leveraging their advanced linguistic capabilities. This paper explores the
potential of LLMs in the entity resolution process, shedding light on both
their advantages and the computational complexities associated with large-scale
matching. We introduce strategies for the efficient utilization of LLMs,
including the selection of an optimal set of matching questions, namely MQsSP,
which is proved to be a NP-hard problem. Our approach optimally chooses the
most effective matching questions while keep consumption limited to your budget
. Additionally, we propose a method to adjust the distribution of possible
partitions after receiving responses from LLMs, with the goal of reducing the
uncertainty of entity resolution. We evaluate the effectiveness of our approach
using entropy as a metric, and our experimental results demonstrate the
efficiency and effectiveness of our proposed methods, offering promising
prospects for real-world applications.

本文研究了大型语言模型在实体解析过程中的潜力，包括利用语言模型的优势以及大规模匹配所涉及的计算复杂性，并提出了一种选择最有效匹配问题的策略，同时限制消耗预算，以减少实体解析的不确定性。通过熵作为度量标准评估我们的方法的效果，实验结果证明了我们提出的方法的效率和有效性，为实际应用提供了有希望的前景。