# Are Language Models Borrowing-Blind? A Multilingual Evaluation of Loanword Identification across 10 Languages

arXiv: https://arxiv.org/abs/2510.26254 · HTML: https://mlsanity.com/paper/2510.26254/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 30 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [All that is English may be Hindi: Enhancing language identification through automatic ranking of likeliness of word borrowing in social media](https://mlsanity.com/paper/1707.08446/) (2017)
- [An Annotated Corpus of Emerging Anglicisms in Spanish Newspaper Headlines](https://mlsanity.com/paper/2004.02929/) (2020)
- [Borrowing or Codeswitching? Annotating for Finer-Grained Distinctions in Language Mixing](https://mlsanity.com/paper/2206.04973/) (2022)
- [Overview of ADoBo at IberLEF 2025: Automatic Detection of Anglicisms in Spanish](https://mlsanity.com/paper/2507.21813/) (2025)
- [HiKE: Hierarchical Evaluation Framework for Korean-English Code-Switching Speech Recognition](https://mlsanity.com/paper/2509.24613/) (2025)
- [Detecting Lexical Borrowings from Dominant Languages in Multilingual Wordlists](https://mlsanity.com/paper/2302.00189/) (2023)

## Built on by (most cited first, 2 of 2)

- [Observatorio Lazaro: A self-populating database of anglicism usage in the Spanish press](https://mlsanity.com/paper/2608.00713/) (2026)
- [Multi-Method Validation of Large Language Model Medical Translation Across High- and Low-Resource Languages](https://mlsanity.com/paper/2603.22642/) (2026)

## Abstract (arXiv)

Throughout language history, words are borrowed from one language to another and gradually become integrated into the recipient's lexicon. Speakers can often differentiate these loanwords from native vocabulary, particularly in bilingual communities where a dominant language continuously imposes lexical items on a minority language. This paper investigates whether pretrained language models, including large language models, possess similar capabilities for loanword identification. We evaluate multiple models across 10 languages. Despite explicit instructions and contextual information, our results show that models perform poorly in distinguishing loanwords from native ones. These findings corroborate previous evidence that modern NLP systems exhibit a bias toward loanwords rather than native equivalents. Our work has implications for developing NLP tools for minority languages and supporting language preservation in communities under lexical pressure from dominant languages.