Summary of Cidar: Culturally Relevant Instruction Dataset For Arabic, by Zaid Alyafeai et al.
CIDAR: Culturally Relevant Instruction Dataset For Arabic
by Zaid Alyafeai, Khalid Almubarak, Ahmed Ashraf, Deema Alnuhait, Saied Alshahrani, Gubran A. Q. Abdulrahman, Gamil Ahmed, Qais Gawah, Zead Saleh, Mustafa Ghaleb, Yousef Ali, Maged S. Al-Shaibani
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 introduces CIDAR, a novel instruction-tuning dataset for Large Language Models (LLMs) that caters to the Arabic language and culture. The dataset is designed to address the bias towards Western culture in existing instruction datasets, which can impact the linguistic structures of non-English languages like Arabic. The authors fine-tune LLMs on CIDAR and demonstrate its cultural relevance through comparison with other models. This work has implications for aligning LLMs with the Arabic culture and enriches research efforts in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a special set of instructions to help teach computers to understand Arabic language and culture better. Right now, most instruction sets are made for English or are based on English-only computer models, which can be biased towards Western ideas. This new dataset, called CIDAR, is the first one that’s specifically designed for Arabic and has been reviewed by humans to make sure it’s culturally accurate. The researchers show that this new set of instructions can help computers better understand and work with Arabic language and culture. |
Keywords
* Artificial intelligence * Instruction tuning
