mlsanityData to 7 Oct 2026

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

arXiv 2407.21330 ↗First posted 31 Jul 2024cs.CLcited by 7

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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.

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

Builds on

5 of 16 references.

References · 5

  1. 2023Full Parameter Fine-tuning for Large Language Models with Limited Resourcescited by 134
  2. 2023Memory-Efficient Fine-Tuning of Compressed Large Language Models via sub-4-bit Integer Quantizationcited by 97
  3. 2021AMMU : A Survey of Transformer-based Biomedical Pretrained Language Modelscited by 32
  4. 2019Improving Pre-Trained Multilingual Models with Vocabulary Expansioncited by 27
  5. 2022Are Pretrained Multilingual Models Equally Fair Across Languages?cited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 2.

7 papers.