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Culture is Not Trivia: Sociocultural Theory for Cultural NLP

Paper · 2025 · Naitian Zhou, David Bamman and Isaac L. Bleaman

Summary

Cultural NLP has grown quickly, but researchers lack a shared definition of culture. They rely instead on stand-ins such as nationality, religion, food or values. In this position paper, Zhou, Bamman and Bleaman argue that the field's recurring weaknesses come from this missing theory. These weaknesses are coarse national categories, narrow coverage of mostly well-represented cultures, and benchmarks that stay fixed while culture changes. The authors break the field's aims into four desiderata, grouped as competence and coverage trade-offs, and review the limitations sections of 57 recent papers. They propose sociocultural linguistics (following Bucholtz and Hall) as a framework that treats culture as an ongoing, enacted process rather than a collection of facts. A case study on indexicality shows that models trained on explicit commentary about cultural practices mainly acquire stereotypes, not genuine competence. The authors conclude that full cultural competence still faces major methodological gaps. They recommend reframing much current work as localization, meaning systems designed for a specific application and audience through participatory design. This makes goals more tractable and boundary choices transparent.

Key points

  • Four desiderata for culturally aware systems: Adaptive and Discerning (competence axis), and Inclusive and Nuanced (coverage axis). Each pair involves a trade-off.
  • The survey covers 57 papers from 2022–2024 that mention culture. Self-reported limitations include:
    • limited cultural coverage (40%)
    • an inadequate cultural proxy (about 37%)
    • missing intragroup variation (28%)
    • stereotypes collected as knowledge (14%)
    • static, non-dynamic benchmarks (12%)
  • Nationality served as the cultural boundary proxy in 36% of the surveyed papers.
  • The indexicality case study argues that learning culture from metapragmatic discourse is largely limited to learning stereotypes.
  • Localization requires naming the application domain, the target audience, and the system's expected role as an interlocutor. Examples include ingredient substitutions in a recipe app versus cultural humility in a healthcare chatbot.

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 California, BerkeleyBerkeley · United States175 works · 227,440 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as Naitian Zhou, David Bamman and Isaac L. Bleaman. 2025. Culture is Not Trivia: Sociocultural Theory for Cultural NLP. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.acl-long.1256

@inproceedings{zhou2025culture,
  title = {{Culture is Not Trivia: Sociocultural Theory for Cultural NLP}},
  author = {Naitian Zhou, David Bamman and Isaac L. Bleaman},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.1256},
  url = {https://doi.org/10.18653/v1/2025.acl-long.1256}
}

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