Evaluation of Small Language Models for Arabic Language Processing

Jumana Alsubhi; Ahmed Alhusayni; Abdulrahman Gharawi; Israa Hamdine; Alshaymaa Allahim; Lamees Alhumaid; Ahmad Shabana; Rafik Madani

arXiv · 2606.21460

Evaluation of Small Language Models for Arabic Language Processing

Jumana Alsubhi·Ahmed Alhusayni·Abdulrahman Gharawi·Israa Hamdine·Alshaymaa Allahim·Lamees Alhumaid·Ahmad Shabana·Rafik Madani

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Abstract

This paper evaluates the performance of twelve Small Language Models (SLMs) on Arabic natural language processing tasks. The study introduces a benchmark of 240 Arabic test items distributed across eight domains and ten language skills, covering both comprehension-oriented and generation-oriented tasks. All models were evaluated under a controlled zero-shot setting using a standardized Arabic-only prompt template. Model responses were assessed through a multi-model LLM-as-a-judge framework involving GPT-4.1 Mini, Claude Haiku 4.5, and DeepSeek-Chat, with scores aggregated across judges and analyzed by task, skill, and model family. The results show that Gemma 3 (12B) achieved the highest overall score (4.548/5), followed by Aya and C4AI Command Arabic. The observed results suggest that model size alone does not explain Arabic SLM performance. Models with stronger Arabic alignment and more reliable instruction-following behavior tended to perform better across tasks. Common failure patterns among lower-performing models include prompt leakage, hallucination, language drift, incomplete generation, and weak task adherence. Overall, the benchmark provides a structured reference for evaluating compact Arabic language models and supports future work on efficient, reliable, and culturally appropriate Arabic AI systems.

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Jumana Alsubhi, Ahmed Alhusayni, Abdulrahman Gharawi, Israa Hamdine, Alshaymaa Allahim, Lamees Alhumaid, Ahmad Shabana, Rafik Madani. 2026-06-19. Evaluation of Small Language Models for Arabic Language Processing. https://arxiv.org/abs/2606.21460

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