CAMEL-Bench: A Comprehensive Arabic LMM Benchmark
Authors: name: Sara Ghaboura, name: Ahmed Heakl, name: Omkar Thawakar, name: Ali Alharthi, name: Ines Riahi, name: Abduljalil Saif, name: Jorma Laaksonen, name: Fahad S. Khan, name: Salman Khan, name: Rao M. Anwer
Organization: Mohamed Bin Zayed University of Artificial Intelligence

Recent years have witnessed a significant interest in developing large
multimodal models (LMMs) capable of performing various visual reasoning and
understanding tasks. This has led to the introduction of multiple LMM
benchmarks to evaluate LMMs on different tasks. However, most existing LMM
evaluation benchmarks are predominantly English-centric. In this work, we
develop a comprehensive LMM evaluation benchmark for the Arabic language to
represent a large population of over 400 million speakers. The proposed
benchmark, named CAMEL-Bench, comprises eight diverse domains and 38
sub-domains including, multi-image understanding, complex visual perception,
handwritten document understanding, video understanding, medical imaging, plant
diseases, and remote sensing-based land use understanding to evaluate broad
scenario generalizability. Our CAMEL-Bench comprises around 29,036 questions
that are filtered from a larger pool of samples, where the quality is manually
verified by native speakers to ensure reliable model assessment. We conduct
evaluations of both closed-source, including GPT-4 series, and open-source
LMMs. Our analysis reveals the need for substantial improvement, especially
among the best open-source models, with even the closed-source GPT-4o achieving
an overall score of 62%. Our benchmark and evaluation scripts are open-sourced.