Year: 2025
Type: article
Abstract: Large Language Models (LLMs) have recently demonstrated outstanding performance in a variety of Natural Language Processing (NLP) tasks. Although many LLMs have been developed, only a few models have been evaluated in the context of the Arabic language, with a significant focus on the ChatGPT model. This study assessed three LLMs on two Arabic NLP tasks: sentiment analysis and machine translation. The capabilities of LLaMA, Mixtral, and Gemma under zero- and few-shot learning were investigated, and their performance was compared against State-Of-The-Art (SOTA) models. The experimental results showed that, among the three models, LLaMA tends to have better comprehension abilities for the Arabic language, outperforming Mixtral and Gemma on both tasks. However, except for the Arabic-to-English translation, where LLaMA outperforms the transformer model by 4 BLEU points, in all cases, the performance of the three LLMs fell behind that of the SOTA model.
DOI: 10.48084/etasr.9584
Authors: Mohamed Zouidine, Mohammed Khalil
Institution: University of Hassan II Casablanca
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $431
Award: G3541559399
OpenAlex ID: W4409173725