Beyond MCQ: An Open-Ended Arabic Cultural QA Benchmark with Dialect Variants
Summary: arXiv:2510.24328v2 Announce Type: replace-cross
Abstract
Large Language Models (LLMs) are increasingly used to answer everyday questions, yet their performance on culturally grounded and dialectal content remains uneven across languages. In response to this challenge, researchers have proposed a comprehensive method aimed at enhancing the understanding and performance of LLMs in Arabic dialects. This method includes several innovative steps:
- Translation of Questions: Modern Standard Arabic (MSA) multiple-choice questions (MCQs) are translated into English and various Arabic dialects.
- Conversion to Open-Ended Questions: The translated MCQs are converted into open-ended questions (OEQs) to evaluate the models’ reasoning capabilities.
- Benchmarking Models: A range of zero-shot and fine-tuned LLMs are benchmarked under both MCQ and OEQ settings to assess their performance.
- Chain-of-Thought Rationales: The method generates chain-of-thought (CoT) rationales to fine-tune models, promoting step-by-step reasoning.
Significance of the Research
This research extends an existing dataset where question-answer pairs are aligned across multiple language varieties. To our knowledge, it is the first of its kind, addressing a significant gap in the field of natural language processing for Arabic dialects. The developed dataset is expected to support further research on culturally and linguistically inclusive evaluation, thereby enriching the understanding of Arabic language models.
Key Findings
Extensive experiments were conducted with both open and closed models, revealing several critical insights:
- Performance Gaps: Models exhibited underperformance when handling Arabic dialects, highlighting persistent gaps in culturally grounded and dialect-specific knowledge.
- MCQ vs. OEQ Performance: Arabic-centric models showed strong performance on MCQs but encountered difficulties with OEQs, suggesting a need for more sophisticated reasoning capabilities.
- Impact of Chain-of-Thought: Implementing CoT improved the judged correctness of responses but yielded mixed results in n-gram-based metrics, indicating a complex relationship between reasoning and linguistic accuracy.
Future Directions
The findings from this research underscore the necessity for further investigations into the performance of LLMs across different Arabic dialects. Moving forward, the research community is encouraged to explore the following:
- Development of additional datasets that encompass a broader range of dialects and cultural contexts.
- Enhancement of model architectures to better accommodate the nuances of dialectal Arabic.
- Collaboration between linguists and AI researchers to ensure that cultural contexts are effectively integrated into language models.
Conclusion
The introduction of an open-ended Arabic cultural QA benchmark marks a significant advancement in the evaluation of LLMs. By focusing on dialectal variants, this research aims to bridge existing gaps and foster a more inclusive understanding of Arabic language processing. The publicly released dataset will undoubtedly serve as a valuable resource for future studies in the field.