# Performance of Recent Large Language Models for a Low-Resourced Language

arXiv: https://arxiv.org/abs/2407.21330 · HTML: https://mlsanity.com/paper/2407.21330/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 31 Jul 2024.

## Builds on (most cited first, 5 of 5)

- [Full Parameter Fine-tuning for Large Language Models with Limited Resources](https://mlsanity.com/paper/2306.09782/) (2023)
- [Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantization](https://mlsanity.com/paper/2305.14152/) (2023)
- [AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models](https://mlsanity.com/paper/2105.00827/) (2021)
- [Improving Pre-Trained Multilingual Models with Vocabulary Expansion](https://mlsanity.com/paper/1909.12440/) (2019)
- [Are Pretrained Multilingual Models Equally Fair Across Languages?](https://mlsanity.com/paper/2210.05457/) (2022)

## Built on by (most cited first, 7 of 7)

- [Exploring Multilingual Probing in Large Language Models: A Cross-Language Analysis](https://mlsanity.com/paper/2409.14459/) (2024)
- [Transcending Language Boundaries: Harnessing LLMs for Low-Resource Language Translation](https://mlsanity.com/paper/2411.11295/) (2024)
- [Better To Ask in English? Evaluating Factual Accuracy of Multilingual LLMs in English and Low-Resource Languages](https://mlsanity.com/paper/2504.20022/) (2025)
- [QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines](https://mlsanity.com/paper/2505.07345/) (2025)
- [Subasa - Adapting Language Models for Low-resourced Offensive Language Detection in Sinhala](https://mlsanity.com/paper/2504.02178/) (2025)
- [Large Language Models for Math Education in Low-Resource Languages: A Study in Sinhala and Tamil](https://mlsanity.com/paper/2602.14517/) (2026)
- [Transfer Learning for Named Entity Recognition of Classical Latin through LLM Prompting](https://mlsanity.com/paper/2608.04015/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have shown significant advances in the past year. In addition to new versions of GPT and Llama, several other LLMs have been introduced recently. Some of these are open models available for download and modification. Although multilingual large language models have been available for some time, their performance on low-resourced languages such as Sinhala has been poor. We evaluated four recent LLMs on their performance directly in the Sinhala language, and by translation to and from English. We also evaluated their fine-tunability with a small amount of fine-tuning data. Claude and GPT 4o perform well out-of-the-box and do significantly better than previous versions. Llama and Mistral perform poorly but show some promise of improvement with fine tuning.