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

• URL: http://arxiv.org/abs/2510.26254v1
• Date: Thu, 30 Oct 2025 08:36:07 GMT
• Title: Language Models Are Borrowing-Blind: A Multilingual Evaluation of Loanword Identification across 10 Languages
• Authors: Mérilin Sousa Silva, Sina Ahmadi,
• Abstract summary: This paper investigates whether pretrained language models, including large language models, possess similar capabilities for loanword identification.<n>Despite explicit instructions and contextual information, our results show that models perform poorly in distinguishing loanwords from native ones.<n>Our work has implications for developing NLP tools for minority languages and supporting language preservation in communities under lexical pressure from dominant languages.
• Score: 2.9740076875957886
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Large Language Models Share Representations of Latent Grammatical Concepts Across Typologically Diverse Languages [15.203789021094982]
  In large language models (LLMs), how are multiple languages learned and encoded?<n>We train sparse autoencoders on Llama-3-8B and Aya-23-8B, and demonstrate that abstract grammatical concepts are often encoded in feature directions shared across many languages.
  arXiv  Detail & Related papers  (2025-01-10T21:18:21Z)
• The Role of Language Imbalance in Cross-lingual Generalisation: Insights from Cloned Language Experiments [57.273662221547056]
  In this study, we investigate an unintuitive novel driver of cross-lingual generalisation: language imbalance. We observe that the existence of a predominant language during training boosts the performance of less frequent languages. As we extend our analysis to real languages, we find that infrequent languages still benefit from frequent ones, yet whether language imbalance causes cross-lingual generalisation there is not conclusive.
  arXiv  Detail & Related papers  (2024-04-11T17:58:05Z)
• Multilingual Word Embeddings for Low-Resource Languages using Anchors and a Chain of Related Languages [54.832599498774464]
  We propose to build multilingual word embeddings (MWEs) via a novel language chain-based approach. We build MWEs one language at a time by starting from the resource rich source and sequentially adding each language in the chain till we reach the target. We evaluate our method on bilingual lexicon induction for 4 language families, involving 4 very low-resource (5M tokens) and 4 moderately low-resource (50M) target languages.
  arXiv  Detail & Related papers  (2023-11-21T09:59:29Z)
• The Less the Merrier? Investigating Language Representation in Multilingual Models [8.632506864465501]
  We investigate the linguistic representation of different languages in multilingual models. We observe from our experiments that community-centered models perform better at distinguishing between languages in the same family for low-resource languages.
  arXiv  Detail & Related papers  (2023-10-20T02:26:34Z)
• NusaWrites: Constructing High-Quality Corpora for Underrepresented and Extremely Low-Resource Languages [54.808217147579036]
  We conduct a case study on Indonesian local languages. We compare the effectiveness of online scraping, human translation, and paragraph writing by native speakers in constructing datasets. Our findings demonstrate that datasets generated through paragraph writing by native speakers exhibit superior quality in terms of lexical diversity and cultural content.
  arXiv  Detail & Related papers  (2023-09-19T14:42:33Z)
• Tokenization Impacts Multilingual Language Modeling: Assessing Vocabulary Allocation and Overlap Across Languages [3.716965622352967]
  We propose new criteria to evaluate the quality of lexical representation and vocabulary overlap observed in sub-word tokenizers. Our findings show that the overlap of vocabulary across languages can be actually detrimental to certain downstream tasks.
  arXiv  Detail & Related papers  (2023-05-26T18:06:49Z)
• XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models [100.29953199404905]
  We introduce a new approach for scaling to very large multilingual vocabularies by de-emphasizing token sharing between languages with little lexical overlap. We train XLM-V, a multilingual language model with a one million token vocabulary. XLM-V is particularly effective on low-resource language tasks and outperforms XLM-R by 11.2% and 5.8% absolute on MasakhaNER and Americas NLI, respectively.
  arXiv  Detail & Related papers  (2023-01-25T09:15:17Z)
• Discovering Representation Sprachbund For Multilingual Pre-Training [139.05668687865688]
  We generate language representation from multilingual pre-trained models and conduct linguistic analysis. We cluster all the target languages into multiple groups and name each group as a representation sprachbund. Experiments are conducted on cross-lingual benchmarks and significant improvements are achieved compared to strong baselines.
  arXiv  Detail & Related papers  (2021-09-01T09:32:06Z)
• Including Signed Languages in Natural Language Processing [48.62744923724317]
  Signed languages are the primary means of communication for many deaf and hard of hearing individuals. This position paper calls on the NLP community to include signed languages as a research area with high social and scientific impact.
  arXiv  Detail & Related papers  (2021-05-11T17:37:55Z)
• Revisiting Language Encoding in Learning Multilingual Representations [70.01772581545103]
  We propose a new approach called Cross-lingual Language Projection (XLP) to replace language embedding. XLP projects the word embeddings into language-specific semantic space, and then the projected embeddings will be fed into the Transformer model. Experiments show that XLP can freely and significantly boost the model performance on extensive multilingual benchmark datasets.
  arXiv  Detail & Related papers  (2021-02-16T18:47:10Z)
• Improving Multilingual Models with Language-Clustered Vocabularies [8.587129426070979]
  We introduce a novel procedure for multilingual vocabulary generation that combines the separately trained vocabularies of several automatically derived language clusters. Our experiments show improvements across languages on key multilingual benchmark tasks.
  arXiv  Detail & Related papers  (2020-10-24T04:49:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.