Lost in Cultural Translation: Do LLMs Struggle with Math Across Cultural Contexts?

Abstract

We demonstrate that large language models' (LLMs) mathematical reasoning is culturally sensitive: testing 14 models from Anthropic, OpenAI, Google, Meta, DeepSeek, Mistral, and Microsoft across six culturally adapted variants of the GSM8K benchmark, we find accuracy drops ranging from 0.3% (Claude 3.5 Sonnet) to 5.9% (LLaMA 3.1-8B) when math problems are embedded in unfamiliar cultural contexts--even when the underlying mathematical logic remains unchanged. These statistically significant performance reductions (p < 0.01, confirmed through McNemar tests) reveal that mathematical reasoning in LLMs is not culturally neutral. To create these variants for Haiti, Moldova, Pakistan, Solomon Islands, Somalia, and Suriname, we systematically replaced cultural entities (names, foods, places, etc.) in 1,198 GSM8K questions while preserving all mathematical operations and numerical values. Our quantitative error analysis of 18,887 instances reveals that cultural adaptation affects broader reasoning patterns, with mathematical reasoning errors comprising 54.7% and calculation errors 34.5% of failures. Interestingly, cultural familiarity can enhance performance: Mistral Saba outperforms some larger models on Pakistan-adapted problems due to Middle Eastern and South Asian training data exposure. This study underscores the need for more diverse training data to ensure robust LLM performance across global contexts.