When adapting language models for entity matching, the model's pretraining variant (embedding-focused vs. general) matters more than raw size, and architectural advantages depend heavily on whether you're dealing with distribution shift or cross-dataset scenarios.
This paper systematically compares how language models perform at entity matching (identifying records referring to the same real-world entity) across different architectures, model variants, and sizes.