---
title: "Culturally Aware and Adapted NLP — paper by Liu et al. (2025)"
description: "They then build a fine-grained taxonomy that extends earlier NLP frameworks such as Hershcovich… 256 references, cited by 65 papers here."
authors: ["Chen Cecilia Liu", "Iryna Gurevych", "Anna Korhonen"]
year: 2025
venue: "Transactions of the Association for Computational Linguistics"
source: "https://aclanthology.org/2025.tacl-1.31/"
doi: "10.1162/tacl_a_00760"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/liu-2025-culturally-aware-adapted-nlp-taxonomy/"
---
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# Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art

**Authors:** Chen Cecilia Liu, [Iryna Gurevych](https://md.research.elamur.ai/free/en/authors/iryna-gurevych/) and [Anna Korhonen](https://md.research.elamur.ai/free/en/authors/anna-korhonen/) · **Year:** 2025 · **Venue:** [Transactions of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/tacl/) ([Transactions of the Association for Computational Linguistics (TACL) 2025](https://md.research.elamur.ai/free/en/venues/tacl/2025/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.tacl-1.31/) · [PDF](https://aclanthology.org/2025.tacl-1.31.pdf) · [DOI 10.1162/tacl\_a\_00760](https://doi.org/10.1162/tacl_a_00760) · [OpenAlex](https://openalex.org/W4412073924)

**Citations:** 24 citations (OpenAlex, 2026-10-14) · [65](https://md.research.elamur.ai/free/en/papers/liu-2025-culturally-aware-adapted-nlp-taxonomy/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Interest in culture within NLP has grown quickly, yet the field still lacks a common idea of what "culture" means, so progress is hard to measure. Liu, Gurevych and Korhonen address this by drawing on anthropology and the social sciences. Their working definition treats culture as shared ideas, language and social practices that arise and change through human interaction. They then build a fine-grained taxonomy that extends earlier NLP frameworks such as Hershcovich et al. (2022) and Hovy and Yang (2021). The taxonomy has three branches: ideational, linguistic and social.

The authors use this framework to sort 127 papers from leading *CL venues into resources and methods. They find that cultural resources encode culture either in the data or in the annotation labels. Most work still treats standard languages or national borders as stand-ins for culture, so linguistic and social variation receives little attention. Adaptation methods are generally underdeveloped. The survey matters because it gives researchers a shared vocabulary and a map of open gaps, including fairness concerns with LLMs.

## Key points
- **Ideational branch:** concepts, knowledge, values, norms & morals, and artifacts. Artifacts are treated as ideational because NLP does not deal with physical objects.
- **Linguistic and social branches:** the linguistic branch covers dialects (e.g., AAVE) and styles/registers/genres. The social branch covers relationships, context, communicative goals and demographics.
- **Coverage and resources:** the survey covers 127 publications from ACL, EMNLP, NAACL, EACL, AACL and TACL. Resource types include knowledge probing, MMLU-style benchmarks, commonsense datasets, WEAT-based bias tests and sentence-pair bias datasets.
- **State of methods:** prompting and continual pretraining get slightly more attention than other approaches. PEFT-based transfer has barely been applied beyond dialects. The authors suggest training PEFT modules for values on WVS survey data.
- **Open directions:**
  - Moving from "surface" to "deep" adaptation, following Resnicow et al. (1999), so that models behave like members of a culture. No work yet measures how deep an adaptation goes.
  - Building culturally diverse preference data and reward models for feedback learning.
  - Modeling culture as it changes over time, for example through retrieval-augmented systems.

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

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- [Technische Universität Darmstadt](https://md.research.elamur.ai/free/en/universities/technische-universitat-darmstadt/) — University, Darmstadt, Germany · 76 works · 35,008 citations
- [Hessian Center for Artificial Intelligence](https://md.research.elamur.ai/free/en/labs/hessian-center-for-artificial-intelligence/) — Research institute, Darmstadt, Germany · 4 works · 297 citations

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## Cite

Cite as: Chen Cecilia Liu, Iryna Gurevych and Anna Korhonen. 2025. Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art. Transactions of the Association for Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{liu2025culturally,
  title = {{Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art}},
  author = {Chen Cecilia Liu, Iryna Gurevych and Anna Korhonen},
  journal = {Transactions of the Association for Computational Linguistics},
  year = {2025},
  doi = {10.1162/tacl\_a\_00760},
  url = {https://doi.org/10.1162/tacl_a_00760}
}
```

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256. We believe that focusing on *CL venues is an appropriate choice for this purpose. [find](https://scholar.google.com/scholar?q=We+believe+that+focusing+on+%2ACL+venues+is+an+appropriate+choice+for+this+purpose%2E)

*Updated 24 October 2026*