Generalized Entity Matching (GEM), which aims at judging whether two records
represented in different formats refer to the same real-world entity, is an
essential task in data management. The prompt tuning paradigm for pre-trained
language models (PLMs), including the recent PromptEM model, effectively
addresses the challenges of low-resource GEM in practical applications,
offering a robust solution when labeled data is scarce. However, existing
prompt tuning models for GEM face the challenges of prompt design and
information gap. This paper introduces an augmented prompt tuning framework for
the challenges, which consists of two main improvements. The first is an
augmented contextualized soft token-based prompt tuning method that extracts a
guiding soft token benefit for the PLMs' prompt tuning, and the second is a
cost-effective information augmentation strategy leveraging large language
models (LLMs). Our approach performs well on the low-resource GEM challenges.
Extensive experiments show promising advancements of our basic model without
information augmentation over existing methods based on moderate-size PLMs
(average 5.24%+), and our model with information augmentation achieves
comparable performance compared with fine-tuned LLMs, using less than 14% of
the API fee.

该研究介绍了一种增强的提示调整框架，用于通用实体匹配中的低资源挑战，包括上下文化软记号提示调整方法和成本效益的信息增强策略。实验结果显示，该方法在低资源实体匹配方面取得了显著的进展。

APrompt4EM：增强提示调优通用实体匹配

APrompt4EM: Augmented Prompt Tuning for Generalized Entity Matching

Entity Matching (EM) refers to the problem of determining whether two
different data representations refer to the same real-world entity. It has been
a long-standing interest of the data management community and many efforts have
been paid in creating benchmark tasks as well as in developing advanced
matching techniques. However, existing benchmark tasks for EM are limited to
the case where the two data collections of entities are structured tables with
the same schema. Meanwhile, the data collections for matching could be
structured, semi-structured, or unstructured in real-world scenarios of data
science. In this paper, we come up with a new research problem -- Generalized
Entity Matching to satisfy this requirement and create a benchmark Machamp for
it. Machamp consists of seven tasks having diverse characteristics and thus
provides good coverage of use cases in real applications. We summarize existing
EM benchmark tasks for structured tables and conduct a series of processing and
cleaning efforts to transform them into matching tasks between tables with
different structures. Based on that, we further conduct comprehensive profiling
of the proposed benchmark tasks and evaluate popular entity matching approaches
on them. With the help of Machamp, it is the first time that researchers can
evaluate EM techniques between data collections with different structures.

本文提出了一种新的名为广义实体匹配的研究问题，并创建了一个名为 Machamp 的基准测试任务集，其中包含七个具有不同特征的任务，以满足数据科学领域中实际应用的需求。基于此，作者综合分析了现有的结构化表格实体匹配基准测试任务，并通过一系列的处理和清理工作将其转换为具有不同结构的表格之间的匹配任务。利用 Machamp，作者对流行的实体匹配方法进行了综合性能评估。这是第一次研究者可以在不同结构的数据集之间对实体匹配技术进行评估。