Nationality and Region Prediction from Names: A Comparative Study of Neural Models and Large Language Models

2026-01-13

AI Summary

This study addresses the challenge of generalization in name-based nationality prediction, particularly for low-frequency countries and geographically proximate regions. It presents the first systematic comparison of six neural network architectures against six large language model (LLM) prompting strategies across three granularity levels: nationality, region, and continent, employing frequency-stratified sampling and fine-grained error analysis. Results demonstrate that LLMs consistently outperform traditional neural models at all granularities, exhibiting exceptional robustness at the regional level. Notably, simpler machine learning approaches show greater resilience for low-frequency nationalities, while LLMs tend to make “neighborhood” errors—confusing geographically adjacent regions—rather than exhibiting cross-regional bias. The work underscores the importance of evaluating error types and quality beyond aggregate accuracy metrics.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Predicting nationality from personal names has practical value in marketing, demographic research, and genealogical studies. Conventional neural models learn statistical correspondences between names and nationalities from task-specific training data, posing challenges in generalizing to low-frequency nationalities and distinguishing similar nationalities within the same region. Large language models (LLMs) have the potential to address these challenges by leveraging world knowledge acquired during pre-training. In this study, we comprehensively compare neural models and LLMs on nationality prediction, evaluating six neural models and six LLM prompting strategies across three granularity levels (nationality, region, and continent), with frequency-based stratified analysis and error analysis. Results show that LLMs outperform neural models at all granularity levels, with the gap narrowing as granularity becomes coarser. Simple machine learning methods exhibit the highest frequency robustness, while pre-trained models and LLMs show degradation for low-frequency nationalities. Error analysis reveals that LLMs tend to make ``near-miss''errors, predicting the correct region even when nationality is incorrect, whereas neural models exhibit more cross-regional errors and bias toward high-frequency classes. These findings indicate that LLM superiority stems from world knowledge, model selection should consider required granularity, and evaluation should account for error quality beyond accuracy.

Problem

• nationality prediction
• region prediction
• name-based inference
• low-frequency nationalities
• cross-regional disambiguation

Innovation

• nationality prediction
• large language models
• neural models
• granularity analysis
• error analysis