MBZUAI researchers introduce Web2Code, a new large-scale dataset and evaluation framework for training and benchmarking multimodal LLMs on webpage understanding and HTML code generation. The dataset includes webpage images, HTML code, and QA pairs about webpage content. Experiments demonstrate the dataset's utility in webpage understanding, code generation, and general visual domain tasks, with code and data available on Github.
MBZUAI researchers have introduced SURPRISE3D, a benchmark for evaluating 3D spatial reasoning in AI systems, along with a 3D Spatial Reasoning Segmentation (3D-SRS) task. The benchmark includes over 900 indoor scenes and 200,000 language queries paired with 3D masks, emphasizing spatial relationships over object naming. A companion paper, MLLM-For3D, explores adapting 2D multimodal LLMs for 3D reasoning. Why it matters: This work addresses a key limitation in current AI, pushing towards embodied AI that can understand and act in 3D environments based on human-like spatial reasoning.
MBZUAI graduates Abdulla and Abdulrahman Almarzooqi are developing AI systems to improve UAE road safety. Abdulla's research focuses on external highway monitoring using MLLMs to analyze driving scenes and generate accident reports, while Abdulrahman's work uses in-cabin sensors to detect driver fatigue and distractions. Together, their systems aim to create a comprehensive view of factors influencing traffic accidents, with potential applications in ADAS and automated accident reporting. Why it matters: This research showcases the potential of AI agents and multimodal LLMs to proactively enhance road safety in the UAE and reduce traffic-related incidents.
MBZUAI is conducting research to improve cross-cultural understanding using AI, including studying LLM limitations in recognizing cultural references. They developed "Culturally Yours," a tool that helps users comprehend cultural references in text, and the "All Languages Matter Benchmark" (ALM Bench) to evaluate multimodal LLMs across 100 languages. MBZUAI has also developed LLMs tailored to low-resource languages like Jais (Arabic), Nanda (Hindi), and Sherkala (Kazakh). Why it matters: These initiatives promote inclusivity and ensure AI systems are culturally aware and can serve diverse populations effectively, particularly in the Middle East's multicultural context.
MBZUAI researchers, in collaboration with over 70 researchers, have created the Culturally diverse Visual Question Answering (CVQA) benchmark to evaluate cultural understanding in multimodal LLMs. The CVQA dataset includes over 10,000 questions in 31 languages and 13 scripts, testing models on images of local dishes, personalities, and monuments. Testing of several multimodal LLMs on the CVQA benchmark revealed significant challenges, even for top models. Why it matters: This benchmark highlights the need for AI models to better understand diverse cultures, promoting fairness and relevance across different languages and regions.
MBZUAI researchers have released ALM Bench, a new benchmark dataset for evaluating the performance of multimodal LLMs on cultural visual question-answer tasks across 100 languages. The dataset includes over 22,000 question-answer pairs across 19 categories, with a focus on low-resource languages and cultural nuances, including three Arabic dialects. They tested 16 open- and closed-source multimodal LLMs on it, revealing a significant need for greater cultural and linguistic inclusivity. Why it matters: The benchmark aims to improve the inclusivity of multimodal AI systems by addressing the underrepresentation of low-resource languages and cultural contexts.
MBZUAI researchers introduced Web2Code, a new dataset suite, at NeurIPS to enhance multimodal LLM performance in web page analysis and HTML generation. The suite includes a fine-tuning dataset and two benchmark datasets. Instruction tuning with Web2Code improved performance on specialized tasks without affecting general capabilities. Why it matters: This contribution addresses a key limitation in current multimodal LLMs, potentially boosting productivity in web design and development by providing targeted training data.