想用LLM做实体匹配？这篇论文用Qwen3对比了bi-encoder、cross-encoder和生成式三种架构，告诉你不同场景选什么最靠谱，还公开了代码和实验数据。
该论文通过控制实验比较了Qwen3家族三种匹配器架构（bi-encoder、cross-encoder、generative matcher）、三种模型变体（embedding-oriented等）和三种模型大小，在9个数据集上进行了1215次微调运行。结果显示，embedding-oriented变体对bi-encoder的初始化与表征几何至关重要。cross-encoder始终优于bi-encoder，但更大的模型可缩小这一差距。生成式匹配器仅在分布偏移（如记录模式或跨数据集迁移）下优于cross-encoder。更大的模型更依赖捷径学习，不一定表现更好。
Beyond Scale and Generation: Understanding Language Model-based Entity Matching
Entity matching identifies records that refer to the same real-world entity. Language models can be adapted to this task through bi-encoder, cross-encoder, and generative matcher architectures. However, prior studies often conflate matcher architecture with differences in model backbone, model variant(reflecting different pretraining objectives), and model size, making it difficult to isolate the sources of performance gains. We address this issue through a controlled factorial study spanning three matcher architectures, three model variants and three model sizes from the Qwen3 family, and nine datasets, totaling 1,215 fine-tuning runs. We also evaluate cross-dataset transferability and computational cost. Our results show that model variant is critical for bi-encoders: embedding-oriented variants provide stronger initialization and more favorable representation geometry predictive of downstream matching performance. Cross-encoders retain a consistent advantage over bi-encoders because they jointly encode record pairs rather than representing each record independently, although larger models partially narrow this gap. Generative matchers do not universally outperform cross-encoders. Instead, their advantages concentrate under distribution shift, including subtle unseen differences in record schemas and cross-dataset transfer. We further find that larger models rely more heavily on shortcut learning and therefore do not necessarily perform better. These findings clarify the factors underlying performance differences across matcher architectures and motivate future research and benchmark designs that better disentangle architectural choices from model-level factors while explicitly evaluating distribution shift and cross-dataset transferability. We release our experimental results, code, training scripts, and evaluation data at https://github.com/Jantory/llm-trained-matcher.