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

arXiv: https://arxiv.org/abs/2604.10401 · HTML: https://mlsanity.com/paper/2604.10401/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 12 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [Measuring short-form factuality in large language models](https://mlsanity.com/paper/2411.04368/) (2024)
- [Predicting Race and Ethnicity From the Sequence of Characters in a Name](https://mlsanity.com/paper/1805.02109/) (2018)
- [Nationality Classification Using Name Embeddings](https://mlsanity.com/paper/1708.07903/) (2017)
- [raceBERT -- A Transformer-based Model for Predicting Race and Ethnicity from Names](https://mlsanity.com/paper/2112.03807/) (2021)
- [Enriching Datasets with Demographics through Large Language Models: What's in a Name?](https://mlsanity.com/paper/2409.11491/) (2024)

## Abstract (arXiv)

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.