Entity · paper

Beyond Scale and Generation: Understanding Language Model-based Entity Matching

paperactiveprovisionalbeyond-scale-and-generation-understanding-language-model-based-entity-matching-9cd40c72·1 events·first seen 2d ago

Aliases: Beyond Scale and Generation: Understanding Language Model-based Entity Matching

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Alibaba Qwen3

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4arXiv · cs.CL·2d ago·source ↗

Controlled factorial study disentangles architecture, model variant, and scale effects in LLM-based entity matching

A new arXiv preprint presents a controlled factorial study of language model-based entity matching across three matcher architectures (bi-encoder, cross-encoder, generative), three model variants, and three model sizes from the Qwen3 family, totaling 1,215 fine-tuning runs on nine datasets. Key findings include: model variant (pretraining objective) is more important than scale for bi-encoders; cross-encoders consistently outperform bi-encoders but larger models narrow the gap; generative matchers only outperform cross-encoders under distribution shift; and larger models are more prone to shortcut learning. The study also evaluates cross-dataset transferability and computational cost, releasing all code and results.

Evaluation and Benchmarking Alibaba Beyond Scale and Generation: Understanding Language Model-based Entity Matching Qwen3