CIDAR: Culturally Relevant Instruction Dataset For Arabic
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
Instruction tuning has emerged as a prominent methodology for teaching Large
Language Models (LLMs) to follow instructions. However, current instruction
datasets predominantly cater to English or are derived from English-dominated
LLMs, resulting in inherent biases toward Western culture. This bias
significantly impacts the linguistic structures of non-English languages such
as Arabic, which has a distinct grammar reflective of the diverse cultures
across the Arab region. This paper addresses this limitation by introducing
CIDAR: https://hf.co/datasets/arbml/CIDAR, the first open Arabic
instruction-tuning dataset culturally-aligned by human reviewers. CIDAR
contains 10,000 instruction and output pairs that represent the Arab region. We
discuss the cultural relevance of CIDAR via the analysis and comparison to
other models fine-tuned on other datasets. Our experiments show that CIDAR can
help enrich research efforts in aligning LLMs with the Arabic culture. All the
code is available at https://github.com/ARBML/CIDAR.