REVIEW 4 cited by
CIDAR: Culturally Relevant Instruction Dataset For Arabic
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
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.
Forward citations
Cited by 4 Pith papers
-
PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture
PalmX 2025 introduces a two-subtask MCQA benchmark for Arabic and Islamic cultural knowledge and shows that task-specific fine-tuning, especially LoRA, improves LLM accuracy.
-
MedArabiQ: Benchmarking Large Language Models on Arabic Medical Tasks
MedArabiQ is a seven-task Arabic medical benchmark showing that closed models generally beat open ones on structured questions, while BERTScore misses serious hallucinations that an LLM judge later reveals.
-
Bridging Language Barriers in Healthcare: A Study on Arabic LLMs
The optimal Arabic-English training-data ratio for a medical LLM varies by task, and fine-tuning alone does not reliably improve Arabic clinical performance.
-
Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic
A 1.6B Arabic language model trained with synthetic multiple-choice instruction data outperforms 7B-13B models on several Arabic multiple-choice benchmarks.
Discussion (0). Continue with ORCID to comment.