Entity Resolution Using Transformer-Based Language Models: A Systematic Scoping Review

Entity resolution (ER) is fundamental to integrating heterogeneous data, which traditional approaches address through deterministic rule-based methods and probabilistic record linkage. We conducted a systematic scoping review following PRISMA-ScR to characterize the use of transformer-based language models for ER. Five databases were searched, and 155 studies were included for synthesis. The literature expanded sharply after 2023, with 55% of included studies published in 2025 or 2026. General-purpose matching was the most common entity focus, followed by product/e-commerce. Healthcare applications were especially scarce, with only one study applying ER to the patient/healthcare domain. Among studies that performed blocking, embedding-based similarity was most common, followed by string-based approaches. Classification-head approaches remained the most common matching approach, followed by prompt-based approaches. Encoder-only models remained the most widely evaluated architecture, while decoder-only models grew increasingly prominent from 2023 onward. Full-parameter fine-tuning was the predominant learning strategy, followed by zero-shot and few-shot prompting. Among studies classified as general-purpose, nearly one-third were evaluated on only one or two entity types, limiting their generalizability. Reported best F1 scores varied across benchmarks, with no consistent advantage for encoder-only versus decoder-only architectures. These findings support the need for more diverse, end-to-end evaluations that consider efficiency and robustness alongside accuracy.