In open-ended generative tasks like narrative writing or dialogue, large language models often exhibit cultural biases, showing limited knowledge and generating templated outputs for less prevalent cultures. Recent works show that these biases may stem from uneven cultural representation in pretraining corpora. This work investigates how pretraining leads to biased culture-conditioned generations by analyzing how models associate entities with cultures based on pretraining data patterns. We propose the MEMOed framework (MEMOrization from pretraining document) to determine whether a generation for a culture arises from memorization. Using MEMOed on culture-conditioned generations about food and clothing for 110 cultures, we find that high-frequency cultures in pretraining data yield more generations with memorized symbols, while some low-frequency cultures produce none. Additionally, the model favors generating entities with extraordinarily high frequency regardless of the conditioned culture, reflecting biases toward frequent pretraining terms irrespective of relevance. We hope that the MEMOed framework and our insights will inspire more works on attributing model performance on pretraining data.

该研究解决了大型语言模型在开放式生成任务中的文化偏见问题，分析了预训练数据对生成结果的影响。提出的MEMOed框架揭示了模型对文化的生成是否源于记忆，发现高频文化的生成更倾向于使用记忆的符号，而低频文化则几乎没有生成。这一发现能推动更多关于模型在预训练数据上表现的研究。

将文化条件的生成归因于预训练语料库