NameBERT: Scaling Name-Based Nationality Classification with LLM-Augmented Open Academic Data

TL;DR

NameBERT leverages large language models to augment datasets for improved nationality classification from names, achieving higher accuracy and efficiency over existing methods.

Contribution

The paper introduces a novel framework that uses LLMs to enrich name datasets, enhancing nationality prediction especially for underrepresented countries.

Findings

Augmentation with LLM-generated names improves tail-country classification accuracy.

NameBERT outperforms state-of-the-art baselines in both in- and out-of-domain tasks.

The approach maintains efficiency suitable for large-scale inference.

Abstract

Inferring nationality from personal names is a critical capability for equity and bias monitoring, personalization, and a valuable tool in biomedical and sociological research. However, existing name-based nationality classifiers are typically trained on relatively small or source-specific labeled datasets, which can introduce coverage gaps and limit performance for underrepresented countries. While large language models (LLMs) demonstrate strong zero-shot performance for name-based nationality prediction, their computational cost and latency make them impractical for real-time, large-scale deployment. In this work, we created a large-scale name-nationality dataset from the Open Academic Graph (OAG) and introduce a framework that leverages LLMs as dataset enrichers rather than inference engines. We augment low-resource countries with LLM-generated names and evaluate on real and synthetic-tail test sets. We find that augmentation produces large gains when evaluation includes synthetic tail names and still offers a modest lift on tail-country metrics otherwise. Overall, NameBERT models achieve significantly higher accuracy than state-of-the-art baselines across both in- and out-of-domain tasks, while remaining efficient for large-scale inference compared to LLMs.