Beyond Scale and Generation: Understanding Language Model-based Entity Matching
Score
81系统性地分离了架构、变体和规模对实体匹配的影响，提供了此前未见的大规模对照实验证据。
实验设计严谨，方法论清晰，结果分析深入，开源了完整资源。
对选择匹配器架构和模型变体有直接指导，但实验结果基于Qwen3系列，泛化性需验证。
2025年最新论文，紧跟当前大模型在结构化数据上的应用热点。
arXiv预印本，但实验设计完整，且开源，可信度较高。
Base 89.5 × T2 Source weight ×0.9 = 81
Curation line 70Selected
Why selected
Rigorous experimental design covering multiple factors, results directly actionable, and open source code/data provided.
AI commentary
「This paper is noteworthy for providing the most rigorous comparison of entity matching architectures to date, especially the counterintuitive conclusion that larger models are not necessarily better. With open-sourced resources, it is highly valuable for practitioners. Teams working on data integration, knowledge graph construction, or structured data processing with LLMs will find this paper helps avoid common pitfalls.」
Quick overview
This paper conducts a controlled factorial study on three matcher architectures (bi-encoder, cross-encoder, generative), three model variants, and three model sizes (from Qwen3 family) with 1,215 fine-tuning runs across nine datasets. Key findings: model variant is critical for bi-encoders; cross-encoders consistently outperform bi-encoders but larger models can narrow the gap; generative matchers do not universally outperform cross-encoders, only excelling under distribution shift or cross-dataset transfer; larger models rely more on shortcut learning and do not necessarily perform better. The study clarifies the interplay of architecture, model variant, and scale in entity matching.
- Controlled 1,215 fine-tuning runs across architecture, model variant, and size
- Model variant is critical for bi-encoder initialization and representation geometry
- Cross-encoders consistently outperform bi-encoders, but larger models narrow the gap
- Generative matchers only excel under distribution shift and cross-dataset transfer; larger models show shortcut learning
Full text
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