National statistical offices (NSOs) increasingly rely on record linkage to link census data, administrative sources, and survey responses. However, conventional string-similarity methods often struggle with free-text fields. To address these challenges, this paper systematically benchmarks modern open-source large language models (LLMs) against classic string-based comparators for record linkage. Building on these findings, this paper introduces a hybrid approach that retains well-established probabilistic frameworks yet integrates an LLM-based classifier for ambiguous record pairs. A Bayesian update is applied to combine the LLM's output with the prior probability, with the aim of reducing the burden on manual clerical review. The experiments show that selectively deploying open-source LLMs for the most uncertain pairs can significantly reduce manual effort by refining decisions through Bayesian updating. As NSOs must ensure transparency, explainability, and adherence to official statistical standards, this paper systematically addresses these concerns while evaluating the potential of LLMs for record linkage. Practical considerations including secure on-premises deployment, computational cost, human-in-the-loop review, and calibration are discussed to support responsible adoption in official statistics.
Hanan Ather (Thu,) studied this question.