# Testing Low-Resource Language Support in LLMs Using Language Proficiency Exams: the Case of Luxembourgish

arXiv: https://arxiv.org/abs/2504.01667 · HTML: https://mlsanity.com/paper/2504.01667/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 51 references that are in the map.
- First posted on arXiv 2 Apr 2025.

## Builds on (most cited first, 16 of 16)

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- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [WizardLM: Empowering large pre-trained language models to follow complex instructions](https://mlsanity.com/paper/2304.12244/) (2023)
- [ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools](https://mlsanity.com/paper/2406.12793/) (2024)
- [EuroLLM: Multilingual Language Models for Europe](https://mlsanity.com/paper/2409.16235/) (2024)
- [Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLU](https://mlsanity.com/paper/2310.04928/) (2023)
- [SciEx: Benchmarking Large Language Models on Scientific Exams with Human Expert Grading and Automatic Grading](https://mlsanity.com/paper/2406.10421/) (2024)
- [Text Generation Models for Luxembourgish with Limited Data: A Balanced Multilingual Strategy](https://mlsanity.com/paper/2412.09415/) (2024)
- [LLMzSzL: a comprehensive LLM benchmark for Polish](https://mlsanity.com/paper/2501.02266/) (2025)
- [Disce aut Deficere: Evaluating LLMs Proficiency on the INVALSI Italian Benchmark](https://mlsanity.com/paper/2406.17535/) (2024)
- [Cracking the Code: Multi-domain LLM Evaluation on Real-World Professional Exams in Indonesia](https://mlsanity.com/paper/2409.08564/) (2024)
- [Evaluating Large Language Models with Tests of Spanish as a Foreign Language: Pass or Fail?](https://mlsanity.com/paper/2409.15334/) (2024)
- [Examining the Behavior of LLM Architectures Within the Framework of Standardized National Exams in Brazil](https://mlsanity.com/paper/2408.05035/) (2024)

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

- [MENLO: From Preferences to Proficiency -- Evaluating and Modeling Native-like Quality Across 47 Languages](https://mlsanity.com/paper/2509.26601/) (2025)
- [LuxIT: A Luxembourgish Instruction Tuning Dataset from Monolingual Seed Data](https://mlsanity.com/paper/2510.24434/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) have become an increasingly important tool in research and society at large. While LLMs are regularly used all over the world by experts and lay-people alike, they are predominantly developed with English-speaking users in mind, performing well in English and other wide-spread languages while less-resourced languages such as Luxembourgish are seen as a lower priority. This lack of attention is also reflected in the sparsity of available evaluation tools and datasets. In this study, we investigate the viability of language proficiency exams as such evaluation tools for the Luxembourgish language. We find that large models such as Claude and DeepSeek-R1 typically achieve high scores, while smaller models show weak performances. We also find that the performances in such language exams can be used to predict performances in other NLP tasks in Luxembourgish.