BEACON在低资源实体匹配上表现不错，但你知道它为啥管用吗？这篇论文给你答案。
论文研究了BEACON框架在低资源、领域感知实体匹配任务中的表现。通过一系列针对性实验，分析了分布对齐策略和数据可用性条件对性能的影响。揭示了不同算法选择如何改变BEACON的行为。
Understanding Domain-Aware Distribution Alignment in Budgeted Entity Matching
Entity Matching (EM) is a core operation in the data integration pipeline, where records from different sources are compared to determine whether they refer to the same real-world entity. Recent work has incorporated domain information and low-resource learning techniques to better adapt EM systems to realistic settings. While these approaches have demonstrated strong performance, it remains unclear how they behave under varying data constraints and levels of supervision in practice. In this paper, we investigate a state-of-the-art method for low-resource, domain-aware EM--BEACON--and study how its performance is affected by different algorithmic choices and data availability conditions. We conduct a series of targeted experiments to evaluate these variations, providing deeper insight into the role of distribution alignment and the behavior of the BEACON framework.