Large language models (LLMs) are reported to be partial to certain cultures
owing to the training data dominance from the English corpora. Since
multilingual cultural data are often expensive to collect, existing efforts
handle this by prompt engineering or culture-specific pre-training. However,
they might overlook the knowledge deficiency of low-resource culture and
require extensive computing resources. In this paper, we propose CultureLLM, a
cost-effective solution to incorporate cultural differences into LLMs.
CultureLLM adopts World Value Survey (WVS) as seed data and generates
semantically equivalent training data via the proposed semantic data
augmentation. Using only 50 seed samples from WVS with augmented data, we
fine-tune culture-specific LLMs and one unified model (CultureLLM-One) for 9
cultures covering rich and low-resource languages. Extensive experiments on 60
culture-related datasets demonstrate that CultureLLM significantly outperforms
various counterparts such as GPT-3.5 (by 8.1%) and Gemini Pro (by 9.5%) with
comparable performance to GPT-4 or even better. Our human study shows that the
generated samples are semantically equivalent to the original samples,
providing an effective solution for LLMs augmentation.

提出了一种经济高效的解决方案 CultureLLM，利用 World Value Survey (WVS) 作为种子数据，通过提议的语义数据增强方法生成语义上等价的训练数据，然后使用这些数据对文化特定的 LLMS 进行微调，以及一个统一模型 (CultureLLM-One) 来覆盖 9 种文化。实验结果表明 CultureLLM 在各种文化相关数据集上的性能明显优于其他对比模型，例如 GPT-3.5（8.1%）和 Gemini Pro（9.5%），且与 GPT-4 相当甚至更好。人类研究结果显示生成的样本在语义上与原样本相等，为 LLMs 的增强提供了有效的解决方案。

CultureLLM: 将文化差异融入大型语言模型

CultureLLM: Incorporating Cultural Differences into Large Language  Models

This paper is a quantitative analysis of the data collected globally by the
World Value Survey. The data is used to study the trajectories of change in
individuals' religious beliefs, values, and behaviors in societies. Utilizing
random forest, we aim to identify the key factors of religiosity and classify
respondents of the survey as religious and non religious using country level
data. We use resampling techniques to balance the data and improve imbalanced
learning performance metrics. The results of the variable importance analysis
suggest that Age and Income are the most important variables in the majority of
countries. The results are discussed with fundamental sociological theories
regarding religion and human behavior. This study is an application of machine
learning in identifying the underlying patterns in the data of 30 countries
participating in the World Value Survey. The results from variable importance
analysis and classification of imbalanced data provide valuable insights
beneficial to theoreticians and researchers of social sciences.

该研究利用全球通过世界价值观调查收集的数据进行定量分析，研究了社会中个体宗教信仰、价值观和行为的变化轨迹。利用随机森林，通过国家级数据识别出宗教性的关键因素，并将调查中的受访者分类为宗教和非宗教。通过平衡数据和改进不平衡学习性能指标的重采样技术，变量重要性分析结果表明年龄和收入是大多数国家中最重要的变量。研究结果与有关宗教和人类行为的基本社会学理论进行了讨论。该研究是在世界价值观调查的 30 个参与国家的数据中识别潜在模式的机器学习应用。变量重要性分析和不平衡数据分类的结果为社会科学的理论家和研究人员提供了有价值的洞察。