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: this https URL, 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 this https URL

这篇论文介绍了 CIDAR，这是第一个由人类评论者文化对齐的开放式阿拉伯语指导调优数据集，CIDAR 拥有 10,000 个指令和输出对，代表着阿拉伯地区，实验证明 CIDAR 可以帮助将 LLMs 与阿拉伯文化相结合。