The intricate relationship between language and culture has long been a
subject of exploration within the realm of linguistic anthropology. Large
Language Models (LLMs), promoted as repositories of collective human knowledge,
raise a pivotal question: do these models genuinely encapsulate the diverse
knowledge adopted by different cultures? Our study reveals that these models
demonstrate greater cultural alignment along two dimensions -- firstly, when
prompted with the dominant language of a specific culture, and secondly, when
pretrained with a refined mixture of languages employed by that culture. We
quantify cultural alignment by simulating sociological surveys, comparing model
responses to those of actual survey participants as references. Specifically,
we replicate a survey conducted in various regions of Egypt and the United
States through prompting LLMs with different pretraining data mixtures in both
Arabic and English with the personas of the real respondents and the survey
questions. Further analysis reveals that misalignment becomes more pronounced
for underrepresented personas and for culturally sensitive topics, such as
those probing social values. Finally, we introduce Anthropological Prompting, a
novel method leveraging anthropological reasoning to enhance cultural
alignment. Our study emphasizes the necessity for a more balanced multilingual
pretraining dataset to better represent the diversity of human experience and
the plurality of different cultures with many implications on the topic of
cross-lingual transfer.

通过调查人类社会学的实际问卷与模型响应的对比，我们的研究发现，大型语言模型 (LLMs) 在两个维度上表现出更高的文化一致性，即当以特定文化的主要语言作为提示时，以及当使用该文化所采用的多语言精炼混合体系进行预训练时，对于模拟调查的不同人物与敏感社会议题，模型的文化一致性更加重要。最后，我们引入了人类学提示的创新方法，利用人类学推理增强文化一致性。我们的研究强调了更平衡的多语言预训练数据集对于更好地代表人类经验多样性和不同文化的复数性，对于跨语言传递的影响的必要性。

研究大型语言模型的文化一致性

Investigating Cultural Alignment of Large Language Models

Instruction tuning has emerged as a prominent methodology for teaching Large
Language Models (LLMs) to follow instructions. However, current instruction
datasets predominantly cater to English or are derived from English-dominated
LLMs, resulting in inherent biases toward Western culture. This bias
significantly impacts the linguistic structures of non-English languages such
as Arabic, which has a distinct grammar reflective of the diverse cultures
across the Arab region. This paper addresses this limitation by introducing
CIDAR: this https URL, the first open Arabic
instruction-tuning dataset culturally-aligned by human reviewers. CIDAR
contains 10,000 instruction and output pairs that represent the Arab region. We
discuss the cultural relevance of CIDAR via the analysis and comparison to
other models fine-tuned on other datasets. Our experiments show that CIDAR can
help enrich research efforts in aligning LLMs with the Arabic culture. All the
code is available at this https URL

这篇论文介绍了 CIDAR，这是第一个由人类评论者文化对齐的开放式阿拉伯语指导调优数据集，CIDAR 拥有 10,000 个指令和输出对，代表着阿拉伯地区，实验证明 CIDAR 可以帮助将 LLMs 与阿拉伯文化相结合。