数学并非无文化属性：通过实体与场景扰动探测文化差异
Mathematics Isn't Culture-Free: Probing Cultural Gaps via Entity and Scenario Perturbations
AI总结：
本研究通过实体与场景扰动为GSM8K构建五个地区的文化适应变体，评估六个8B至72B大语言模型发现，模型在原始美式数据集上表现最佳，但推理能力有助于弥合数学问题的文化呈现差距。
摘要：
尽管数学常被视为在文化上是中立的，但数学问题的呈现方式可能带有隐含的文化背景。现有基准（如GSM8K）主要根植于西方规范，包括姓名、货币和日常场景。在本研究中，我们利用基于提示的转换并结合人工验证，为非洲、印度、中国、韩国和日本五个地区创建了GSM8K测试集的文化适应变体。我们评估了六个大型语言模型（LLMs），参数规模从8B到72B不等，采用五种提示策略，以评估它们对数学问题呈现中文化变化的鲁棒性。研究结果显示出一致的性能差距：模型在以美国为中心的原始数据集上表现最佳，在文化适应版本上表现相对较差。然而，具备推理能力的模型对这些转变更具弹性，这表明更深入的推理有助于弥合数学任务中的文化呈现差距。
英文摘要：
Although mathematics is often considered culturally neutral, the way mathematical problems are presented can carry implicit cultural context. Existing benchmarks like GSM8K are predominantly rooted in Western norms, including names, currencies, and everyday scenarios. In this work, we create culturally adapted variants of the GSM8K test set for five regions Africa, India, China, Korea, and Japan using prompt-based transformations followed by manual verification. We evaluate six large language models (LLMs), ranging from 8B to 72B parameters, across five prompting strategies to assess their robustness to cultural variation in math problem presentation. Our findings reveal a consistent performance gap: models perform best on the original US-centric dataset and comparatively worse on culturally adapted versions. However, models with reasoning capabilities are more resilient to these shifts, suggesting that deeper reasoning helps bridge cultural presentation gaps in mathematical tasks
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
