Researchers show LLMs can link records and infer ethnicity from names without training
A new paper on arXiv demonstrates that pre-trained language models can perform record linkage and demographic inference from names without needing task-specific training data. The author introduces EnsembleLink, an algorithm combining pre-trained embeddings with string-based scorers to match records across scripts and formats, achieving performance comparable to or better than prior methods. For…
Key points
- EnsembleLink algorithm matches records across scripts/formats with performance equal to or better than prior methods
- LLMs achieve **83.8%** accuracy on U.S. voter files for ethnicity inference, surpassing **78.0%** benchmark
- Open-weight models replicate proprietary LLM performance, reducing reliance on labeled training data
The paper highlights that pre-trained models encode much of the world knowledge needed for these tasks, minimizing the need for labeled data. Open-weight models were tested and found to approach the performance of proprietary LLMs, offering reproducible implementations for researchers.
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