TY - GEN
T1 - CIDAR
T2 - Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024
AU - Alyafeai, Zaid
AU - Almubarak, Khalid
AU - Ashraf, Ahmed
AU - Alnuhait, Deema
AU - Alshahrani, Saied
AU - Abdulrahman, Gubran A.Q.
AU - Ahmed, Gamil
AU - Gawah, Qais
AU - Saleh, Zead
AU - Ghaleb, Mustafa
AU - Ali, Yousef
AU - Al-Shaibani, Maged S.
N1 - Publisher Copyright:
© 2024 Association for Computational Linguistics.
PY - 2024
Y1 - 2024
N2 - 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, leading to inherent biases toward Western culture. This bias negatively impacts non-English languages such as Arabic and the unique culture of the Arab region. This paper addresses this limitation by introducing CIDAR, the first open Arabic instruction-tuning dataset culturally aligned by native Arabic speakers. 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 a few models fine-tuned on other datasets. Our experiments indicate that models fine-tuned on CIDAR achieve better cultural alignment compared to those fine-tuned on 30x more data. The dataset is available on HuggingFace https://huggingface.co/datasets/arbml/CIDAR.
AB - 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, leading to inherent biases toward Western culture. This bias negatively impacts non-English languages such as Arabic and the unique culture of the Arab region. This paper addresses this limitation by introducing CIDAR, the first open Arabic instruction-tuning dataset culturally aligned by native Arabic speakers. 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 a few models fine-tuned on other datasets. Our experiments indicate that models fine-tuned on CIDAR achieve better cultural alignment compared to those fine-tuned on 30x more data. The dataset is available on HuggingFace https://huggingface.co/datasets/arbml/CIDAR.
UR - https://www.scopus.com/pages/publications/85205308917
M3 - Conference contribution
AN - SCOPUS:85205308917
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 12878
EP - 12901
BT - 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024 - Proceedings of the Conference
A2 - Ku, Lun-Wei
A2 - Martins, Andre
A2 - Srikumar, Vivek
PB - Association for Computational Linguistics (ACL)
Y2 - 11 August 2024 through 16 August 2024
ER -