Summary of Peacock: a Family Of Arabic Multimodal Large Language Models and Benchmarks, by Fakhraddin Alwajih et al.
Peacock: A Family of Arabic Multimodal Large Language Models and Benchmarks
by Fakhraddin Alwajih, El Moatez Billah Nagoudi, Gagan Bhatia, Abdelrahman Mohamed, Muhammad Abdul-Mageed
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Multimodal large language models (MLLMs) have shown promise in various tasks, but their success is largely limited to English-based settings due to a lack of high-quality multimodal resources in languages like Arabic. To address this challenge, we introduce Peacock, a family of Arabic MLLMs with strong vision and language capabilities. Our comprehensive analysis demonstrates the solid performance of these models on visual reasoning tasks and their emerging dialectal potential. Additionally, we propose Henna, a new benchmark for assessing MLLMs on aspects related to Arabic culture. These developments set the stage for culturally-aware Arabic MLLMs, which can be explored further through our GitHub repository. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about special computers that can understand and process lots of different types of information, like pictures and words. These computers are really good at doing tasks that require thinking and understanding language. But the problem is that there aren’t many resources available in languages other than English for these computers to learn from. For example, Arabic-speaking countries have a large population, but there isn’t much data available for these computers to use. To fix this issue, the researchers created special computer models called Peacock that can understand and process Arabic information. They tested these models on various tasks and showed that they work really well. The researchers also came up with new ways to measure how good these computer models are. |
