Summary of A Survey Of Large Language Models For Arabic Language and Its Dialects, by Malak Mashaabi et al.
A Survey of Large Language Models for Arabic Language and its Dialects
by Malak Mashaabi, Shahad Al-Khalifa, Hend Al-Khalifa
First submitted to arxiv on: 26 Oct 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 This paper provides a comprehensive overview of Large Language Models (LLMs) designed specifically for Arabic language and its dialects. The survey covers various architectures, including encoder-only, decoder-only, and encoder-decoder models, as well as the datasets used for pre-training, which span Classical Arabic, Modern Standard Arabic, and Dialectal Arabic. The study also explores monolingual, bilingual, and multilingual LLMs, analyzing their architectures and performance across downstream tasks like sentiment analysis, named entity recognition, and question answering. Additionally, it assesses the openness of Arabic LLMs based on factors such as source code availability, training data, model weights, and documentation. The survey highlights the need for more diverse dialectal datasets and emphasizes the importance of openness for research reproducibility and transparency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at special language models designed just for Arabic language and its different forms. It shows how these models are built and what they can do, like understanding emotions, recognizing important words, and answering questions. The study also talks about making these models more open to other researchers, so they can use them again and learn from each other. Overall, the paper wants to help create better language models that work for all types of Arabic. |
Keywords
» Artificial intelligence » Decoder » Encoder » Encoder decoder » Named entity recognition » Question answering
