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When Bigger Isn’t Better: Evaluating LLMs for Arabic Sentiment Analysis

Year: 2026

Type: conference-paper

Abstract: 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.

Source: Language Resources and Evaluation Conference (LREC)

DOI: 10.63317/2kimof4u6y8x

Authors: Mohamed Ibrahim, Abdullah Makki, Youssef Barakat, Nour Samy, Sarah AlHumoud

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7166532752

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