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

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.

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Publication Details

Published
2024-08-04
DOI
https://doi.org/10.1109/ialp63756.2024.10661169
Citations
10
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
3.47
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article

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

Ravindu Jayakody, Gihan Dias
10 citations
Topic Modeling
3.47
article

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

Ravindu Jayakody, Gihan Dias
article en
10 citations

Abstract

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.

University of Moratuwa (LK)
Quality Education
Openalex Percentile: Top 7%
Topic Modeling
3.47
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