On the effectiveness of limited-data large language model fine-tuning for Arabic

This paper presents an investigation into fine-tuning large language models (LLMs) for Arabic natural language processing (NLP) tasks. Although recent multilingual LLMs have made remarkable progress in zero-shot and few-shot settings, specialized models such as fine-tuned BERT variants continue to define state-of-the-art (SOTA) performance in many Arabic tasks. We demonstrate that by fine-tuning a general-purpose LLM (GPT-4o mini) on only a small subset (3.0%–7.5%) of the training samples, we exceed previous best reported results in sentiment analysis (ArSAS) and sarcasm detection (ArSarcasm), while achieving performance statistically comparable to the SOTA in news categorization (ASND). This study highlights that LLMs, when properly adapted, can outperform established models without relying on full-scale annotated training sets. Furthermore, our analysis with the open-source Gemma-3-27B model confirms the generalizability of our data-efficient method. Notably, this approach enabled the model to achieve performance statistically comparable to SOTA on all three tasks, although the proprietary GPT-4o mini maintained an overall performance advantage. We further compare GPT-4o with GPT-4o mini to examine the impact of model size on fine-tuning. GPT-4o outperforms GPT-4o mini across all sample sizes but by small margins (