# Large Language Models for Zero-Shot Multicultural Name Recognition

arXiv: https://arxiv.org/abs/2507.04149 · HTML: https://mlsanity.com/paper/2507.04149/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 30 references that are in the map.
- First posted on arXiv 5 Jul 2025.

## Builds on (most cited first, 14 of 14)

- [Named Entity Recognition with Bidirectional LSTM-CNNs](https://mlsanity.com/paper/1511.08308/) (2015)
- [The Debate Over Understanding in AI's Large Language Models](https://mlsanity.com/paper/2210.13966/) (2022)
- [Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey](https://mlsanity.com/paper/2412.20367/) (2024)
- [SCORE: Story Coherence and Retrieval Enhancement for AI Narratives](https://mlsanity.com/paper/2503.23512/) (2025)
- [MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration](https://mlsanity.com/paper/2506.19835/) (2025)
- [Draw ALL Your Imagine: A Holistic Benchmark and Agent Framework for Complex Instruction-based Image Generation](https://mlsanity.com/paper/2505.24787/) (2025)
- [Improving Cross-modal Alignment for Text-Guided Image Inpainting](https://mlsanity.com/paper/2301.11362/) (2023)
- [Style-Aware Contrastive Learning for Multi-Style Image Captioning](https://mlsanity.com/paper/2301.11367/) (2023)
- [Deep Learning for Multi-Label Learning: A Comprehensive Survey](https://mlsanity.com/paper/2401.16549/) (2024)
- [VisLingInstruct: Elevating Zero-Shot Learning in Multi-Modal Language Models with Autonomous Instruction Optimization](https://mlsanity.com/paper/2402.07398/) (2024)
- [Divide-Then-Aggregate: An Efficient Tool Learning Method via Parallel Tool Invocation](https://mlsanity.com/paper/2501.12432/) (2025)
- [SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning](https://mlsanity.com/paper/2210.03963/) (2022)
- [Liberating Seen Classes: Boosting Few-Shot and Zero-Shot Text Classification via Anchor Generation and Classification Reframing](https://mlsanity.com/paper/2405.03565/) (2024)
- [A survey of textual cyber abuse detection using cutting-edge language models and large language models](https://mlsanity.com/paper/2501.05443/) (2025)

## Built on by (most cited first, 1 of 1)

- [Who Does This Name Remind You of ? Nationality Prediction via Large Language Model Associative Memory](https://mlsanity.com/paper/2601.12771/) (2026)

## Abstract (arXiv)

The robust and accurate recognition of multicultural names, particularly those not previously encountered, is a critical challenge in an increasingly globalized digital landscape. Traditional methods often falter when confronted with the vast diversity and novel permutations of names across different linguistic and cultural backgrounds. This paper introduces a novel framework, Prompt-Engineered Fine-Tuning (PEFT) for Large Language Models (LLMs) with Adversarial Data Augmentation and Cultural Knowledge Graph Integration, designed to significantly enhance zero-shot multicultural name recognition. Our approach leverages the powerful linguistic understanding of pre-trained LLMs, transforming the recognition task into a guided generation problem. Through meticulous prompt engineering, dynamic integration of explicit cultural knowledge derived from knowledge graphs, and the strategic application of adversarial data augmentation, we equip the LLM with an unprecedented ability to infer the cultural origin of unseen names. Extensive experiments demonstrate that our PEFT method consistently outperforms established deep learning baselines, including advanced Bi-LSTM models with cultural tags, achieving an impressive 93.1% overall accuracy and a remarkable 89.5% accuracy on challenging zero-shot name identification. An in-depth ablation study confirms the synergistic contribution of each component, while a human evaluation highlights our method's performance approaching human expert judgment. This work signifies a substantial leap in multicultural name recognition, offering a highly effective and scalable solution for real-world applications.