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Survey of Cultural Awareness in Language Models: Text and Beyond

Paper · 2025 · Siddhesh Pawar, Junyeong Park, Jiho Jin et al.

Summary

Large language models now sit behind chatbots, assistants, search and recommendation tools used around the world. Their training data, however, come mostly from Western sources. As a result, they can stereotype, flatten identities or respond insensitively to users from other backgrounds.

This survey maps how the NLP, computer vision and HCI communities have tried to make both text-only and multimodal systems (image, video, audio) more culturally aware. The authors first ground the idea of culture in anthropology and psychology. They rely mainly on White (1959) and add Hall's (1976) contrast between high- and low-context communication.

Literature was gathered from arXiv, Google Scholar and the ACL Anthology and filtered with PRISMA, giving more than 300 works. The review covers:

  • data sources and dataset construction,
  • alignment and fine-tuning strategies,
  • benchmarks and regional coverage,
  • ethics and the role of HCI,
  • the use of culturally aligned models in social science.

A recurring finding is that evaluation still leans on multiple-choice tests of explicit knowledge, while implicit cues in open dialogue go largely unmeasured. The work serves as a structured entry point and research agenda for building culturally inclusive AI.

Key points

  • Scope: 300+ papers selected via the PRISMA procedure. Benchmarks are considered from 2016 onward and method papers on pretrained LLMs from 2018 onward. Sources include ACL venues, EMNLP, ICLR, ICML, ICCV, CVPR, CHI and CSCW.
  • Conceptual framework: culture is located along three dimensions:
    • inside individuals (beliefs, norms, knowledge),
    • in interaction between people,
    • in material artifacts such as tools and art.
  • Evaluation gap: multiple-choice questions dominate. Long-form generation is mostly judged by humans or LLM-as-a-judge, and robust culture-specific automatic metrics are missing.
  • Vision data: web-scraped images suffer from availability, apprehension and negative set biases. The authors propose alternatives such as photographing objects with guidance from locals and anthropologists, or using frames from regional TV and video.
  • Future directions:
    • balance culture-specific models with broad cross-cultural ones,
    • study real users' reactions to cultural (mis)alignment.
  • Resources: a companion paper list is available at github.com/siddheshih/culture-awareness-llms. The article is published under the CC BY-NC-ND 4.0 license.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of CopenhagenCopenhagen · Denmark78 works · 14,228 citations🎓Korea Advanced Institute of Science and TechnologyDaejeon · South Korea17 works · 2,673 citations

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 worksSocial Sciences · 744 worksSafety Research · 221 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1,343 works

Cite

Cite as Siddhesh Pawar, Junyeong Park, Jiho Jin, Arnav Arora, Junho Myung, Srishti Yadav, Faiz Ghifari Haznitrama, Inhwa Song, Alice Oh and Isabelle Augenstein. 2025. Survey of Cultural Awareness in Language Models: Text and Beyond. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1162/coli.a.14

@article{pawar2025survey,
  title = {{Survey of Cultural Awareness in Language Models: Text and Beyond}},
  author = {Siddhesh Pawar, Junyeong Park, Jiho Jin, Arnav Arora, Junho Myung, Srishti Yadav, Faiz Ghifari Haznitrama, Inhwa Song, Alice Oh and Isabelle Augenstein},
  journal = {Computational Linguistics},
  year = {2025},
  doi = {10.1162/coli.a.14},
  url = {https://doi.org/10.1162/coli.a.14}
}

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