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Beyond MCQ: An Open-Ended Arabic Cultural QA Benchmark with Dialect Variants

Large Language Models (LLMs) are increasingly used to answer everyday questions, yet their performance on culturally grounded and dialectal content remains limited across languages and their varieties. We propose a comprehensive method that (i) translates Modern Standard Arabic (MSA) multiple-choice questions (MCQs) into English and several Arabic dialects, (ii) converts them into open-ended questions (OEQs), (iii) benchmarks a range of zero-shot and fine-tuned LLMs under both MCQ and OEQ settings, and (iv) generates chain-of-thought (CoT) rationales to fine-tune models for step-by-step reasoning. Using this method, we extend an existing dataset in which QAs are parallelly aligned across language varieties, making it, to our knowledge, the first of its kind. A large portion of the resulting test set is further validated through targeted human annotation and native-speaker post-editing. We conduct extensive experiments with both open and closed models. Our findings show that (i) models underperform on Arabic dialects, showing persistent gaps in culturally grounded and dialect-specific knowledge; (ii) Arabic-centric models perform well on MCQs but struggle with OEQs; and (iii) CoT improves judged correctness while yielding mixed n-gram-based metrics.