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When Bigger Isn’t Better: Evaluating LLMs for Arabic Sentiment Analysis
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When Bigger Isn’t Better: Evaluating LLMs for Arabic Sentiment Analysis
This study evaluates the performance of a fine-tuned Arabic sentiment transformer (CAMeL-MSA) against eight large language models (LLMs). Using zero-shot prompting across six Arabic sentiment datasets, we compare a specialized, task-specific approach against generalized model capabilities. Results show that the fine-tuned baseline substantially outperformed all LLMs on five of the six datasets in both accuracy and Macro F1-score. While LLMs offer versatility, this comparison highlights the continued practical superiority of task-specific fine-tuning over zero-shot prompting.
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