Papers by Gamil Ahmed
CIDAR: Culturally Relevant Instruction Dataset For Arabic (2024.findings-acl)
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Zaid Alyafeai, Khalid Almubarak, Ahmed Ashraf, Deema Alnuhait, Saied Alshahrani, Gubran Abdulrahman, Gamil Ahmed, Qais Gawah, Zead Saleh, Mustafa Ghaleb, Yousef Ali, Maged Al-shaibani
| Challenge: | Instruction tuning datasets predominantly cater to English or are derived from English-dominated LLMs. |
| Approach: | They propose to use an Arabic instruction tuning dataset culturally aligned by native Arabic speakers to address drawbacks of finetuning LLMs on machine-generated or machinetranslated datasets. |
| Outcome: | The proposed datasets show that they achieve better cultural alignment than models fine-tuned on other datasets. |