---
title: "Cultural Adaptation in Large Language Models for Political"
description: "The author defines three levels of adaptation: translation, discourse and ontology. 31 references."
authors: ["Wajdi Zaghouani"]
year: 2026
venue: "Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences (PoliticalNLP 2026)"
source: "https://aclanthology.org/2026.politicalnlp-1.29/"
doi: "10.63317/2prsxx26bfis"
licence: "CC BY-NC 4.0 (ELRA, LREC proceedings)"
licence_url: "https://creativecommons.org/licenses/by-nc/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zaghouani-2026-cultural-adaptation-large-language-models/"
---
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# Cultural Adaptation in Large Language Models for Political Discourse

**Authors:** [Wajdi Zaghouani](https://md.research.elamur.ai/free/en/authors/wajdi-zaghouani/) · **Year:** 2026 · **Venue:** [Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences (PoliticalNLP 2026)](https://md.research.elamur.ai/free/en/venues/politicalnlp/) ([Workshop on Natural Language Processing for Political Sciences 2026](https://md.research.elamur.ai/free/en/venues/politicalnlp/2026/)) · **Publisher:** [European Language Resources Association (ELRA)](https://md.research.elamur.ai/free/en/publishers/european-language-resources-association-elra/) · **Licence:** [CC BY-NC 4.0 (ELRA, LREC proceedings)](https://creativecommons.org/licenses/by-nc/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.politicalnlp-1.29/) · [PDF](https://aclanthology.org/2026.politicalnlp-1.29.pdf) · [DOI 10.63317/2prsxx26bfis](https://doi.org/10.63317/2prsxx26bfis) · [OpenAlex](https://openalex.org/W7166569025)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Large language models increasingly support moderation, translation, summarisation and analysis of political communication. However, they learn mostly from English-dominant data and carry assumptions tied to a small set of political systems and rhetorical traditions. This position paper argues that trustworthy political use across countries requires deliberate cultural adaptation. The author defines three levels of adaptation: translation, discourse and ontology. Current models handle translation fairly well and imitate discourse patterns only partly. They are least reliable at the ontology level, which is also the level most tied to democratic legitimacy. Drawing on text-as-data political science, cross-cultural pragmatics and algorithmic auditing, the paper does three things:
- It catalogues systematic cultural failure modes.
- It proposes an evaluation matrix organised around cultural fidelity, calibration and democratic safety.
- It outlines participatory dataset building, selective culturally aware transfer learning and purpose-built diagnostic benchmarks.

The contribution is primarily conceptual. It gives developers and evaluators of political NLP outside Anglophone settings a structured way to make cultural adequacy measurable and to define governance limits on deployment.

## Key points
- **Three-level framework:** translation-level adaptation preserves propositional content, discourse-level adaptation covers genre and pragmatic norms, and ontology-level adaptation represents political concepts and institutions in locally grounded ways.
- **Recurring failure modes:**
  - pragmatic misreading, where irony, satire or honorifics are read literally;
  - genre mismatch, where sermons, testimony or poetry are judged against parliamentary or news norms;
  - concept collapse, which the author links to Sartori's (1970) critique of "concept stretching".
- **Illustrative cases:**
  - Wolof *demokaraasi* in Senegal (Schaffer, 1998) stresses collective security over electoral choice.
  - Arabic *shura* carries religious and communal authority that "deliberation" lacks.
  - Multi-dialectal Arabic stance work (Charfi et al., 2024b) shows that label schemes do not transfer reliably even within one language family.
- **Benchmark gap:** general multilingual benchmarks like XTREME do not test political ontologies, pragmatics or genre variation, so the author calls for domain-specific diagnostic suites.
- **Venue:** PoliticalNLP 2026 workshop at LREC 2026 (Palma, pp. 272–283). The paper ties its recommendations to datasheets, data statements, model cards, community data-governance principles and post-deployment auditing (Raji et al., 2020).

*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

- [Northwestern University in Qatar](https://md.research.elamur.ai/free/en/universities/northwestern-university-in-qatar/) — University, Doha, Qatar · 39 works · 148 citations

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## Cite

Cite as: Wajdi Zaghouani. 2026. Cultural Adaptation in Large Language Models for Political Discourse. Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences (PoliticalNLP 2026).

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

```bibtex
@inproceedings{zaghouani2026cultural,
  title = {{Cultural Adaptation in Large Language Models for Political Discourse}},
  author = {Wajdi Zaghouani},
  booktitle = {Proceedings of the 3rd Workshop on Natural Language Processing for Political Sciences (PoliticalNLP 2026)},
  year = {2026},
  doi = {10.63317/2prsxx26bfis},
  url = {https://doi.org/10.63317/2prsxx26bfis}
}
```

- [← previous Cultivating Forensic Reasoning for Generalizable Multimodal Manipulation Detection](https://md.research.elamur.ai/free/en/papers/zhang-2026-cultivating-forensic-reasoning-generalizable-multimodal/)
- [next → Cultural and Knowledge Biases in LLMs through the Lens of Entity-Aware Machine Translation](https://md.research.elamur.ai/free/en/papers/xu-2026-cultural-knowledge-biases-llms-through/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/28/)

## References

31 references, 9 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Bender, E. M. and Friedman, B. (2018). Data statements for natural language processing: Toward Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). [BERT: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/).
2. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, pages 4171–4186. [find](https://scholar.google.com/scholar?q=In+Proceedings+of+the+2019+Conference+of+the+North+American+Chapter+of+the+Association+for+Computational+Linguistics%2C+pages+4171%E2%80%934186%2E)
3. Fishkin, J. S. (2009). When the People Speak: Deliberative Democracy and Public Consultation. Oxford University Press. [find](https://scholar.google.com/scholar?q=Fishkin%2C+J%2E+S%2E+%282009%29%2E+When+the+People+Speak%3A+Deliberative+Democracy+and+Public+Consultation%2E+Oxford+University+Press%2E)
4. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., and Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12):86–92. [find](https://scholar.google.com/scholar?q=Gebru%2C+T%2E%2C+Morgenstern%2C+J%2E%2C+Vecchione%2C+B%2E%2C+Vaughan%2C+J%2E+W%2E%2C+Wallach%2C+H%2E%2C+Daum%C3%A9+III%2C+H%2E%2C+and+Crawford%2C+K%2E+%282021%29%2E+Datasheets+for+datasets%2E+Communications+of+the+ACM%2C+64%2812%29%3A86%E2%80%9392%2E)
5. Gentzkow, M., Kelly, B., and Taddy, M. (2019). Text as data. Journal of Economic Literature, 57(3):535–574. [find](https://scholar.google.com/scholar?q=Gentzkow%2C+M%2E%2C+Kelly%2C+B%2E%2C+and+Taddy%2C+M%2E+%282019%29%2E+Text+as+data%2E+Journal+of+Economic+Literature%2C+57%283%29%3A535%E2%80%93574%2E)
6. Grimmer, J. and Stewart, B. M. (2013). Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis, 21(3):267–297. [find](https://scholar.google.com/scholar?q=Grimmer%2C+J%2E+and+Stewart%2C+B%2E+M%2E+%282013%29%2E+Text+as+data%3A+The+promise+and+pitfalls+of+automatic+content+analysis+methods+for+political+texts%2E+Political+Analysis%2C+21%283%29%3A267%E2%80%93297%2E)
7. Henrich, J., Heine, S. J., and Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences, 33(2-3):61–83. [find](https://scholar.google.com/scholar?q=Henrich%2C+J%2E%2C+Heine%2C+S%2E+J%2E%2C+and+Norenzayan%2C+A%2E+%282010%29%2E+The+weirdest+people+in+the+world%3F+Behavioral+and+Brain+Sciences%2C+33%282%2D3%29%3A61%E2%80%9383%2E)
8. Hovy, D. and Spruit, S. L. (2016). [The social impact of natural language processing](https://md.research.elamur.ai/free/en/papers/hovy-2016-social-impact-natural-language-processing/). In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, pages 591–598.
9. Hu, J., Ruder, S., Siddhant, A., Neubig, G., Firat, O., and Johnson, M. (2020). XTREME: A massively multilingual multi-task benchmark for evaluating cross-lingual generalisation. In International Conference on Machine Learning, pages 4411–4421. [find](https://scholar.google.com/scholar?q=Hu%2C+J%2E%2C+Ruder%2C+S%2E%2C+Siddhant%2C+A%2E%2C+Neubig%2C+G%2E%2C+Firat%2C+O%2E%2C+and+Johnson%2C+M%2E+%282020%29%2E+XTREME%3A+A+massively+multilingual+multi%2Dtask+benchmark+for+evaluating+cross%2Dlingual+generalisation%2E+In+International+Conf)
10. Joshi, P., Santy, S., Budhiraja, A., Bali, K., and Choudhury, M. (2020). [The state and fate of linguistic diversity and inclusion in the NLP world](https://md.research.elamur.ai/free/en/papers/joshi-2020-state-fate-linguistic-diversity-inclusion/). In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6282–6293.
11. Laver, M., Benoit, K., and Garry, J. (2003). Extracting policy positions from political texts using words as data. American Political Science Review, 97(2):311–331. [find](https://scholar.google.com/scholar?q=Laver%2C+M%2E%2C+Benoit%2C+K%2E%2C+and+Garry%2C+J%2E+%282003%29%2E+Extracting+policy+positions+from+political+texts+using+words+as+data%2E+American+Political+Science+Review%2C+97%282%29%3A311%E2%80%93331%2E)
12. Lowe, W. (2008). Understanding Wordscores. Political Analysis, 16(4):356–371. [find](https://scholar.google.com/scholar?q=Lowe%2C+W%2E+%282008%29%2E+Understanding+Wordscores%2E+Political+Analysis%2C+16%284%29%3A356%E2%80%93371%2E)
13. Lundberg, S. M. and Lee, S.-I. (2017). [A unified approach to interpreting model predictions](https://md.research.elamur.ai/free/en/papers/lundberg-2017-unified-approach-interpreting-model-predictions/). In Advances in Neural Information Processing Systems, volume 30.
14. Mansbridge, J., Bohman, J., Chambers, S., Christiano, T., Fung, A., Parkinson, J., Thompson, D. F., and Warren, M. E. (2012). A systemic approach to deliberative democracy. In Deliberative Systems, pages 1–26. Cambridge University Press. [find](https://scholar.google.com/scholar?q=Mansbridge%2C+J%2E%2C+Bohman%2C+J%2E%2C+Chambers%2C+S%2E%2C+Christiano%2C+T%2E%2C+Fung%2C+A%2E%2C+Parkinson%2C+J%2E%2C+Thompson%2C+D%2E+F%2E%2C+and+Warren%2C+M%2E+E%2E+%282012%29%2E+A+systemic+approach+to+deliberative+democracy%2E+In+Deliberative+Systems%2C+pa)
15. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., and Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency, pages 220–229. [find](https://scholar.google.com/scholar?q=Mitchell%2C+M%2E%2C+Wu%2C+S%2E%2C+Zaldivar%2C+A%2E%2C+Barnes%2C+P%2E%2C+Vasserman%2C+L%2E%2C+Hutchinson%2C+B%2E%2C+Spitzer%2C+E%2E%2C+Raji%2C+I%2E+D%2E%2C+and+Gebru%2C+T%2E+%282019%29%2E+Model+cards+for+model+reporting%2E+In+Proceedings+of+the+Conference+on+Fair)
16. Paullada, A., Raji, I. D., Bender, E. M., Denton, E., and Hanna, A. (2021). Data and its (dis)contents: A survey of dataset development and use in machine learning research. Patterns, 2(11):100336. [find](https://scholar.google.com/scholar?q=Paullada%2C+A%2E%2C+Raji%2C+I%2E+D%2E%2C+Bender%2C+E%2E+M%2E%2C+Denton%2C+E%2E%2C+and+Hanna%2C+A%2E+%282021%29%2E+Data+and+its+%28dis%29contents%3A+A+survey+of+dataset+development+and+use+in+machine+learning+research%2E+Patterns%2C+2%2811%29%3A100336%2E)
17. Pennycook, G. and Rand, D. G. (2019). Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition, 188:39–50. [find](https://scholar.google.com/scholar?q=Pennycook%2C+G%2E+and+Rand%2C+D%2E+G%2E+%282019%29%2E+Lazy%2C+not+biased%3A+Susceptibility+to+partisan+fake+news+is+better+explained+by+lack+of+reasoning+than+by+motivated+reasoning%2E+Cognition%2C+188%3A39%E2%80%9350%2E)
18. Pires, T., Schlinger, E., and Garrette, D. (2019). [How multilingual is multilingual BERT?](https://md.research.elamur.ai/free/en/papers/pires-2019-how-multilingual-is-multilingual-bert/) In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4996–5001.
19. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., and Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability,… [find](https://scholar.google.com/scholar?q=Raji%2C+I%2E+D%2E%2C+Smart%2C+A%2E%2C+White%2C+R%2E+N%2E%2C+Mitchell%2C+M%2E%2C+Gebru%2C+T%2E%2C+Hutchinson%2C+B%2E%2C+Smith%2DLoud%2C+J%2E%2C+Theron%2C+D%2E%2C+and+Barnes%2C+P%2E+%282020%29%2E+Closing+the+AI+accountability+gap%3A+Defining+an+end%2Dto%2Dend+framework+fo)
20. Ribeiro, M. T., Singh, S., and Guestrin, C. (2016). [“Why should I trust you?” Explaining the predictions of any classifier](https://md.research.elamur.ai/free/en/papers/ribeiro-2016-why-should-i-trust-explaining/). In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1135–1144.
21. Sartori, G. (1970). Concept misformation in comparative politics. American Political Science Review, 64(4):1033–1053. [find](https://scholar.google.com/scholar?q=Sartori%2C+G%2E+%281970%29%2E+Concept+misformation+in+comparative+politics%2E+American+Political+Science+Review%2C+64%284%29%3A1033%E2%80%931053%2E)
22. Schaffer, F. C. (1998). Democracy in Translation: Understanding Politics in an Unfamiliar Culture. Cornell University Press. [find](https://scholar.google.com/scholar?q=Schaffer%2C+F%2E+C%2E+%281998%29%2E+Democracy+in+Translation%3A+Understanding+Politics+in+an+Unfamiliar+Culture%2E+Cornell+University+Press%2E)
23. Shu, K., Sliva, A., Wang, S., Tang, J., and Liu, H. (2017). Fake news detection on social media: A data mining perspective. SIGKDD Explorations Newsletter, 19(1):22–36. [find](https://scholar.google.com/scholar?q=Shu%2C+K%2E%2C+Sliva%2C+A%2E%2C+Wang%2C+S%2E%2C+Tang%2C+J%2E%2C+and+Liu%2C+H%2E+%282017%29%2E+Fake+news+detection+on+social+media%3A+A+data+mining+perspective%2E+SIGKDD+Explorations+Newsletter%2C+19%281%29%3A22%E2%80%9336%2E)
24. Slapin, J. B. and Proksch, S.-O. (2008). A scaling model for estimating time-series party positions from texts. American Journal of Political Science, 52(3):705–722. [find](https://scholar.google.com/scholar?q=Slapin%2C+J%2E+B%2E+and+Proksch%2C+S%2E%2DO%2E+%282008%29%2E+A+scaling+model+for+estimating+time%2Dseries+party+positions+from+texts%2E+American+Journal+of+Political+Science%2C+52%283%29%3A705%E2%80%93722%2E)
25. Strubell, E., Ganesh, A., and McCallum, A. (2019). [Energy and policy considerations for deep learning in NLP](https://md.research.elamur.ai/free/en/papers/strubell-2019-energy-policy-considerations-deep-learning/). In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3645–3650.
26. Vamvas, J. and Sennrich, R. (2020). X-Stance: A multilingual multi-target dataset for stance detection. In Proceedings of the 5th Swiss Text Analytics Conference (SwissText) & 16th Conference on Natural Language Processing (KONVENS). [find](https://scholar.google.com/scholar?q=Vamvas%2C+J%2E+and+Sennrich%2C+R%2E+%282020%29%2E+X%2DStance%3A+A+multilingual+multi%2Dtarget+dataset+for+stance+detection%2E+In+Proceedings+of+the+5th+Swiss+Text+Analytics+Conference+%28SwissText%29+%26+16th+Conference+on+Natur)
27. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. (2017). [Attention is all you need](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/). In Advances in Neural Information Processing Systems, volume 30.
28. Vosoughi, S., Roy, D., and Aral, S. (2018). The spread of true and false news online. Science, 359(6380):1146–1151. [find](https://scholar.google.com/scholar?q=Vosoughi%2C+S%2E%2C+Roy%2C+D%2E%2C+and+Aral%2C+S%2E+%282018%29%2E+The+spread+of+true+and+false+news+online%2E+Science%2C+359%286380%29%3A1146%E2%80%931151%2E)
29. Wierzbicka, A. (1991). Cross-Cultural Pragmatics: The Semantics of Human Interaction. Mouton de Gruyter. [find](https://scholar.google.com/scholar?q=Wierzbicka%2C+A%2E+%281991%29%2E+Cross%2DCultural+Pragmatics%3A+The+Semantics+of+Human+Interaction%2E+Mouton+de+Gruyter%2E)
30. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3:160018. [find](https://scholar.google.com/scholar?q=Wilkinson%2C+M%2E+D%2E%2C+Dumontier%2C+M%2E%2C+Aalbersberg%2C+I%2E+J%2E%2C+Appleton%2C+G%2E%2C+Axton%2C+M%2E%2C+Baak%2C+A%2E%2C+Blomberg%2C+N%2E%2C+Boiten%2C+J%2E%2DW%2E%2C+da+Silva+Santos%2C+L%2E+B%2E%2C+Bourne%2C+P%2E+E%2E%2C+et+al%2E+%282016%29%2E+The+FAIR+Guiding+Principles+f)
31. Zaghouani, W., Mubarak, H., and Biswas, M. R. (2024). [So hateful! Building a multi-label hate speech annotated Arabic dataset](https://md.research.elamur.ai/free/en/papers/zaghouani-2024-so-hateful-building-multi-label/). In Proceedings of LREC-COLING 2024, pages 15044– 15055.

*Updated 2 November 2026*