CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting
<|reference_start|>CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting: As the utilization of large language models (LLMs) has proliferated world-wide, it is crucial for them to have adequate knowledge and fair representation for diverse global cultures. In this work, we uncover culture perceptions of three SOTA models on 110 countries and regions on 8 culture-related topics through culture-conditioned generations, and extract symbols from these generations that are associated to each culture by the LLM. We discover that culture-conditioned generation consist of linguistic "markers" that distinguish marginalized cultures apart from default cultures. We also discover that LLMs have an uneven degree of diversity in the culture symbols, and that cultures from different geographic regions have different presence in LLMs' culture-agnostic generation. Our findings promote further research in studying the knowledge and fairness of global culture perception in LLMs. Code and Data can be found here: https://github.com/huihanlhh/Culture-Gen/<|reference_end|>
@article{li2024culture-gen:,
    title={CULTURE-GEN: Revealing Global Cultural Perception in Language Models
  through Natural Language Prompting},
    author={Huihan Li, Liwei Jiang, Jena D. Hwang, Hyunwoo Kim, Sebastin Santy,
  Taylor Sorensen, Bill Yuchen Lin, Nouha Dziri, Xiang Ren, Yejin Choi},
    journal={arXiv preprint arXiv:2404.10199},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10199},
    primaryClass={cs.CL cs.AI}
}