The Open Arabic LLM Leaderboard (OALL) has been launched to benchmark Arabic language models, addressing the gap in resources for non-English NLP. It incorporates datasets like AlGhafa, ACVA, and translated versions of MMLU and EXAMS from the AceGPT suite. The leaderboard uses normalized log likelihood accuracy for tasks, built around HuggingFace’s LightEval framework. Why it matters: This initiative promotes research and development in Arabic NLP, serving over 380 million Arabic speakers by enhancing the evaluation and improvement of Arabic LLMs.
The paper introduces SaudiCulture, a new benchmark for evaluating the cultural competence of LLMs within Saudi Arabia, covering five major geographical regions and diverse cultural domains. The benchmark includes questions of varying complexity and distinguishes between common and specialized regional knowledge. Evaluations of five LLMs (GPT-4, Llama 3.3, FANAR, Jais, and AceGPT) revealed performance declines on region-specific questions, highlighting the need for region-specific knowledge in LLM training.
This research evaluates LLMs like ChatGPT, Llama, Aya, Jais, and ACEGPT on Arabic automated essay scoring (AES) using the AR-AES dataset. The study uses zero-shot, few-shot learning, and fine-tuning approaches while using a mixed-language prompting strategy. ACEGPT performed best among the LLMs with a QWK of 0.67, while a smaller BERT model achieved 0.88. Why it matters: The study highlights challenges faced by LLMs in processing Arabic and provides insights into improving LLM performance in Arabic NLP tasks.
Researchers developed Atlas-Chat, a collection of LLMs for dialectal Arabic, focusing on Moroccan Arabic (Darija). They constructed an instruction dataset by consolidating existing Darija language resources and translating English instructions. Atlas-Chat models (2B, 9B, 27B) outperform state-of-the-art and Arabic-specialized LLMs like LLaMa, Jais, and AceGPT on Darija NLP tasks. Why it matters: This work addresses the gap in LLM support for low-resource Arabic dialects, providing a methodology for instruction-tuning and benchmarks for future research.
Researchers introduce AraDiCE, a benchmark for Arabic Dialect and Cultural Evaluation, comprising seven synthetic datasets in various dialects and Modern Standard Arabic (MSA). The benchmark includes approximately 45,000 post-edited samples and evaluates LLMs on dialect comprehension, generation, and cultural awareness across the Gulf, Egypt, and Levant. Results show that Arabic-specific models like Jais and AceGPT outperform multilingual models on dialectal tasks, but challenges remain in dialect identification, generation, and translation. Why it matters: This benchmark and associated datasets will help improve LLMs' ability to understand and generate diverse Arabic dialects and cultural contexts, addressing a significant gap in current models.
The paper introduces AraTrust, a new benchmark for evaluating the trustworthiness of LLMs when prompted in Arabic. The benchmark contains 522 multiple-choice questions covering dimensions like truthfulness, ethics, safety, and fairness. Experiments using AraTrust showed that GPT-4 performed the best, while open-source models like AceGPT 7B and Jais 13B had lower scores. Why it matters: This benchmark addresses a critical gap in evaluating LLMs for Arabic, which is essential for ensuring the safe and ethical deployment of AI in the Arab world.
Researchers introduce AceGPT, a localized large language model (LLM) specifically for Arabic, addressing cultural sensitivity and local values not well-represented in mainstream models. AceGPT incorporates further pre-training with Arabic texts, supervised fine-tuning using native Arabic instructions and GPT-4 responses, and reinforcement learning with AI feedback using a reward model attuned to local culture. Evaluations demonstrate that AceGPT achieves state-of-the-art performance among open Arabic LLMs across several benchmarks. Why it matters: This work advances culturally-aware AI development for Arabic-speaking communities, providing a valuable resource and benchmark for future research.