LAraBench introduces a benchmark for Arabic NLP and speech processing, evaluating LLMs like GPT-3.5-turbo, GPT-4, BLOOMZ, Jais-13b-chat, Whisper, and USM. The benchmark covers 33 tasks across 61 datasets, using zero-shot and few-shot learning techniques. Results show that SOTA models generally outperform LLMs in zero-shot settings, though larger LLMs with few-shot learning reduce the gap. Why it matters: This benchmark helps assess and improve the performance of LLMs on Arabic language tasks, highlighting areas where specialized models still excel.
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.
Researchers from the National Center for AI in Saudi Arabia investigated the sensitivity of Large Language Model (LLM) leaderboards to minor benchmark perturbations. They found that small changes, like choice order, can shift rankings by up to 8 positions. The study recommends hybrid scoring and warns against over-reliance on simple benchmark evaluations, providing code for further research.