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AraReasoner: Evaluating Reasoning-Based LLMs for Arabic NLP

arXiv · · Significant research

NLP LLM Research Arabic AI Benchmarking

Summary

This paper benchmarks reasoning-focused LLMs, especially DeepSeek models, on fifteen Arabic NLP tasks. The study uses zero-shot, few-shot, and fine-tuning strategies. Key findings include that three in-context examples improve F1 scores by over 13 points on classification tasks, DeepSeek outperforms GPT-4-mini by 12 F1 points on complex inference tasks in the zero-shot setting, and LoRA fine-tuning yields up to an additional 8 points in F1 and BLEU. Why it matters: The systematic evaluation provides insights into the performance of LLMs on Arabic NLP, highlighting the effectiveness of different strategies for improving performance and contributing to the development of more capable Arabic language models.

Keywords

Arabic NLP · LLM · DeepSeek · Reasoning · Fine-tuning

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