Benchmarking Arabic Dialects for Math Problems

Noor Abo Mokh, Teresa Lynn, Nizar Habash et al.1 min readarXivOct 20, 2026

Key Takeaway for Students

Mathematical reasoning remains underexplored for low-resource languages, including Dialectal Arabic. To bridge this gap, we introduce the DAR-MGSM (Dialectal Arabic Multilingual Grade School Math) Benchmark, the first manually curated benchmark for evaluating mathematical reasoning in Dialectal Arabic. DAR-MGSM consists of math word problems (MWPs) requiring multi-step reasonin

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This paper highlights the critical need for robust evaluation benchmarks in low-resource languages, specifically for mathematical reasoning in Dialectal Arabic. For ML engineers, it underscores the challenges and opportunities in developing and evaluating LLMs for diverse linguistic and cultural contexts beyond high-resource languages, impacting model fairness and utility globally.

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Career Insight

high demand

Why it matters

Skills to learn

Design and implement multilingual evaluation benchmarksTechniques for cultural localization of NLP datasetsFine-tuning strategies for low-resource language modelsEvaluating mathematical reasoning capabilities of LLMsData curation and annotation for dialectal variations

Interview questions

Describe the challenges in evaluating mathematical reasoning for low-resource, dialectal languages compared to high-resource languages like English. How would you address them?
Given a pre-trained multilingual LLM, what fine-tuning strategies would you employ to improve its performance on a specific dialectal math word problem benchmark like DAR-MGSM?
Design a system for continuously evaluating and improving an LLM's performance on a benchmark that includes both translated-only and culturally localized versions of problems. What metrics would you track, and how would you prioritize improvements?

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