The paper introduces AraHalluEval, a new framework for evaluating hallucinations in Arabic and multilingual large language models (LLMs). The framework uses 12 fine-grained hallucination indicators across generative question answering and summarization tasks, evaluating 12 LLMs including Arabic-specific, multilingual, and reasoning-based models. Results show factual hallucinations are more common than faithfulness errors, with the Arabic model Allam showing lower hallucination rates. Why it matters: This work addresses a critical gap in Arabic NLP by providing a comprehensive tool for assessing and mitigating hallucination in LLMs, which is essential for reliable AI applications in the Arabic-speaking world.
The paper introduces AraGPT2, a suite of pre-trained transformer models for Arabic language generation, with the largest model (AraGPT2-mega) containing 1.46 billion parameters. Trained on a large Arabic corpus of internet text and news, AraGPT2-mega demonstrates strong performance in synthetic news generation and zero-shot question answering. To address the risk of misuse, the authors also released a discriminator model with 98% accuracy in detecting AI-generated text. Why it matters: This release of both the model and discriminator fills a critical gap in Arabic NLP and encourages further research and applications in the field.
MBZUAI researchers release JEEM, a new benchmark dataset for evaluating vision-language models on Arabic dialects. The dataset covers image captioning and visual question answering tasks using images from Jordan, UAE, Egypt, and Morocco. Results show models struggle with cultural understanding and relevance despite fluent language generation.