Summary of Camel-bench: a Comprehensive Arabic Lmm Benchmark, by Sara Ghaboura et al.
CAMEL-Bench: A Comprehensive Arabic LMM Benchmark
by Sara Ghaboura, Ahmed Heakl, Omkar Thawakar, Ali Alharthi, Ines Riahi, Abduljalil Saif, Jorma Laaksonen, Fahad S. Khan, Salman Khan, Rao M. Anwer
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel benchmark for evaluating large multimodal models (LMMs) on various visual reasoning and understanding tasks in Arabic, aiming to represent the large population of over 400 million speakers. The proposed CAMEL-Bench comprises eight domains and 38 sub-domains, including multi-image understanding, complex visual perception, and handwritten document understanding, among others. The benchmark consists of around 29,036 questions, filtered from a larger pool and verified by native speakers to ensure reliable model assessment. Evaluations are conducted on both closed-source (GPT-4 series) and open-source LMMs, revealing the need for substantial improvement, particularly among the best open-source models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to test computer programs that understand images in Arabic. Right now, most tests for these programs are focused on English-speaking countries. But there are over 400 million people who speak Arabic as their first language! To help create better understanding of images for these people, the researchers made an Arabic-specific test with lots of different types of questions. They tested two kinds of computer programs and found that even the best ones need to improve. The goal is to make computers better at understanding images in Arabic, which can help with all sorts of things like recognizing objects or reading documents. |
Keywords
» Artificial intelligence » Gpt
