---
title: "Cultural Alignment in Large Language Models: An Explanatory Analysis — paper by Masoud et al. (2025)"
description: "The authors propose Hofstede's CAT (Cultural Alignment Test), which gives Llama 2, GPT-3.5 and GPT-4 the VSM13… 54 references, cited by 65 papers here."
authors: ["Reem I. Masoud", "Ziquan Liu", "Martin Ferianc", "Philip Treleaven", "Miguel Rodrigues"]
year: 2025
venue: "Proceedings of the 31st International Conference on Computational Linguistics"
source: "https://aclanthology.org/2025.coling-main.567/"
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/masoud-2025-cultural-alignment-large-language-models/"
---
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# Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions

**Authors:** [Reem I. Masoud](https://md.research.elamur.ai/free/en/authors/reem-i-masoud/), Ziquan Liu, Martin Ferianc, [Philip Treleaven](https://md.research.elamur.ai/free/en/authors/philip-colin-treleaven/) and [Miguel Rodrigues](https://md.research.elamur.ai/free/en/authors/miguel-rodrigues/) · **Year:** 2025 · **Venue:** [Proceedings of the 31st International Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 2025](https://md.research.elamur.ai/free/en/venues/coling/2025/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2025.coling-main.567/) · [PDF](https://aclanthology.org/2025.coling-main.567.pdf) · [OpenAlex](https://openalex.org/W4387030568)

**Citations:** 8 citations (OpenAlex, 2026-10-14) · [65](https://md.research.elamur.ai/free/en/papers/masoud-2025-cultural-alignment-large-language-models/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
LLMs tend to reflect the values of Western societies, but there are few tools for explaining *where* a model's cultural profile departs from a target culture. The authors propose Hofstede's CAT (Cultural Alignment Test), which gives Llama 2, GPT-3.5 and GPT-4 the VSM13 survey. Each model answers in English, Arabic and Chinese, and also while role-playing a person from a given country. The answers are converted into Hofstede's six dimension indices. Kendall Tau then measures how closely the resulting rankings of three cultures (Arab countries, China and the US) match rankings from real survey data. Additional experiments vary sampling settings and compare English and Chinese fine-tuned Llama 2 variants.

All three models struggle to rank cultures correctly. GPT-4 adapts best to personas and is most accurate for China. It is weak for the US and problematic for Arab countries. Fine-tuning language changes Llama 2's cultural answers. These results matter because misalignment can undermine trust and slow AI adoption outside the West. The framework offers a dimension-level diagnostic for building more culturally aware models.

## Key points
- **Survey and dimensions:** The method uses VSM13, which has 24 cultural items and 6 demographic items on a 5-point Likert scale. Responses are mapped to PDI, UAI, IDV, MAS, LTO and IVR through weighted differences of question means.
- **Evaluation procedure:** Every question is run with seeds 1–5, and the results are averaged. Kendall Tau compares the model's country rankings with the ground-truth rankings for each dimension.
- **Sampling settings:** Temperature and top-p were tested only on GPT-3.5. The default setting (temperature 0.5, top-p 0) drifted furthest from the expected dimensions and pushed answers toward neutral.
- **Fine-tuning language:** Llama-2-13b-chat-hf (English) and Llama-2-Chinese-13b-chat diverged on questions about national pride, how often one is happy, and helping friends. The English model declined to answer the national-pride items.
- **Resources:** The code and materials are available at github.com/reemim/Hofstedes_CAT.

*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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## Cite

Cite as: Reem I. Masoud, Ziquan Liu, Martin Ferianc, Philip Treleaven and Miguel Rodrigues. 2025. Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions. Proceedings of the 31st International Conference on Computational Linguistics.

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

```bibtex
@inproceedings{masoud2025cultural,
  title = {{Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions}},
  author = {Reem I. Masoud, Ziquan Liu, Martin Ferianc, Philip Treleaven and Miguel Rodrigues},
  booktitle = {Proceedings of the 31st International Conference on Computational Linguistics},
  year = {2025},
  url = {https://aclanthology.org/2025.coling-main.567/}
}
```

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## References

54 references, 19 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. David H Ackley, Geoffrey E Hinton, and Terrence J Sejnowski. 1985. A learning algorithm for boltzmann machines. Cognitive science, 9(1):147–169. [find](https://scholar.google.com/scholar?q=David+H+Ackley%2C+Geoffrey+E+Hinton%2C+and+Terrence+J+Sejnowski%2E+1985%2E+A+learning+algorithm+for+boltzmann+machines%2E+Cognitive+science%2C+9%281%29%3A147%E2%80%93169%2E)
2. Shihanah Almutairi, Michael Heller, and Dorothy Yen. 2020. Reclaiming the heterogeneity of the arab states. Cross Cultural & Strategic Management, ahead-of-print. [find](https://scholar.google.com/scholar?q=Shihanah+Almutairi%2C+Michael+Heller%2C+and+Dorothy+Yen%2E+2020%2E+Reclaiming+the+heterogeneity+of+the+arab+states%2E+Cross+Cultural+%26+Strategic+Management%2C+ahead%2Dof%2Dprint%2E)
3. Shihanah Almutairi, Michael Heller, and Dorothy Yen. 2021. Reclaiming the heterogeneity of the arab states. [find](https://scholar.google.com/scholar?q=Shihanah+Almutairi%2C+Michael+Heller%2C+and+Dorothy+Yen%2E+2021%2E+Reclaiming+the+heterogeneity+of+the+arab+states%2E)
4. Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al. 2023. Palm 2 technical report. arXiv preprint arXiv:2305.10403. [our card](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
5. Arnav Arora, Lucie-Aimée Kaffee, and Isabelle Augenstein. 2022. Probing pre-trained language models for cross-cultural differences in values. arXiv preprint arXiv:2203.13722. [our card](https://md.research.elamur.ai/free/en/papers/arora-2023-probing-pre-trained-language-models/)
6. Robert H Bennett III, Paul A Fadil, and Robin T Greenwood. 1994. Cultural alignment in response to strategic organizational change: New considerations for a change framework. Journal of Managerial Issues, pages 474–490. [find](https://scholar.google.com/scholar?q=Robert+H+Bennett+III%2C+Paul+A+Fadil%2C+and+Robin+T+Greenwood%2E+1994%2E+Cultural+alignment+in+response+to+strategic+organizational+change%3A+New+considerations+for+a+change+framework%2E+Journal+of+Managerial+Iss)
7. Sjoerd Beugelsdijk and Chris Welzel. 2018. Dimensions and dynamics of national culture: Synthesizing hofstede with inglehart. Journal of Cross-Cultural Psychology, 49(10):1469–1505. PMID: 30369633. [find](https://scholar.google.com/scholar?q=Sjoerd+Beugelsdijk+and+Chris+Welzel%2E+2018%2E+Dimensions+and+dynamics+of+national+culture%3A+Synthesizing+hofstede+with+inglehart%2E+Journal+of+Cross%2DCultural+Psychology%2C+49%2810%29%3A1469%E2%80%931505%2E+PMID%3A+30369633%2E)
8. Bernd Bohnet, Vinh Q Tran, Pat Verga, Roee Aharoni, Daniel Andor, Livio Baldini Soares, Jacob Eisenstein, Kuzman Ganchev, Jonathan Herzig, Kai Hui, et al. 2022. Attributed question answering: Evaluation and modeling for attributed large language models. arXiv preprint arXiv:2212.08037. [our card](https://md.research.elamur.ai/free/en/papers/bohnet-2022-attributed-question-answering-evaluation-modeling/)
9. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020. Language models are few-shot learners. Advances in neural information processing systems, 33:1877–1901. [our card](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
10. Yong Cao, Li Zhou, Seolhwa Lee, Laura Cabello, Min Chen, and Daniel Hershcovich. 2023. Assessing cross-cultural alignment between chatgpt and human societies: An empirical study. arXiv preprint arXiv:2303.17466. [our card](https://md.research.elamur.ai/free/en/papers/cao-2023-assessing-cross-cultural-alignment-between/)
11. Eva Cetinic. 2022. The myth of culturally agnostic ai models. arXiv preprint arXiv:2211.15271. — [arXiv:2211.15271](https://arxiv.org/abs/2211.15271) [find](https://scholar.google.com/scholar?q=Eva+Cetinic%2E+2022%2E+The+myth+of+culturally+agnostic+ai+models%2E+arXiv+preprint+arXiv%3A2211%2E15271%2E+%E2%80%94+%5BarXiv%3A2211%2E15271%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2211%2E15271%29)
12. Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022. Palm: Scaling language modeling with pathways. arXiv preprint arXiv:2204.02311. [our card](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
13. Paula Czarnowska, Yogarshi Vyas, and Kashif Shah. 2021. Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics. Transactions of the Association for Computational Linguistics, 9:1249–1267. [our card](https://md.research.elamur.ai/free/en/papers/czarnowska-2021-quantifying-social-biases-nlp-generalization/)
14. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. [our card](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/)
15. Esin Durmus, Karina Nyugen, Thomas I Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, et al. 2023. Towards measuring the representation of subjective global opinions in language models. arXiv preprint arXiv:2306.16388. [our card](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/)
16. Jennifer Elias. 2023. Google’s newest a.i. model uses nearly five times more text data for training than its predecessor. [find](https://scholar.google.com/scholar?q=Jennifer+Elias%2E+2023%2E+Google%E2%80%99s+newest+a%2Ei%2E+model+uses+nearly+five+times+more+text+data+for+training+than+its+predecessor%2E)
17. EVS. 2011. Evs - european values study 1981 - integrated dataset. GESIS Data Archive, Cologne. ZA4438 Data file Version 3.0.0, https://doi.org/10.4232/1.10791. — [doi:10.4232/1.10791](https://doi.org/10.4232/1.10791) [↗](https://doi.org/10.4232/1.10791)
18. Shangbin Feng, Chan Young Park, Yuhan Liu, and Yulia Tsvetkov. 2023. From pretraining data to language models to downstream tasks: Tracking the trails of political biases leading to unfair NLP models. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume… [our card](https://md.research.elamur.ai/free/en/papers/feng-2023-pretraining-data-language-models-downstream/)
19. Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, et al. 2022. Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned. arXiv preprint arXiv:2209.07858. [our card](https://md.research.elamur.ai/free/en/papers/ganguli-2022-red-teaming-language-models-reduce/)
20. Nikhil Garg, Londa Schiebinger, Dan Jurafsky, and James Zou. 2018. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences, 115(16):E3635– E3644. [find](https://scholar.google.com/scholar?q=Nikhil+Garg%2C+Londa+Schiebinger%2C+Dan+Jurafsky%2C+and+James+Zou%2E+2018%2E+Word+embeddings+quantify+100+years+of+gender+and+ethnic+stereotypes%2E+Proceedings+of+the+National+Academy+of+Sciences%2C+115%2816%29%3AE3635%E2%80%93+)
21. Nelson F Liu, Tianyi Zhang, and Percy Liang. 2023. Evaluating verifiability in generative search engines. arXiv preprint arXiv:2304.09848. [our card](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-verifiability-generative-search-engines/)
22. Helen Gouldner. 1975. The Nature of Human Values. Social Forces, 53(4):659–660. [find](https://scholar.google.com/scholar?q=Helen+Gouldner%2E+1975%2E+The+Nature+of+Human+Values%2E+Social+Forces%2C+53%284%29%3A659%E2%80%93660%2E)
23. Barbara Marshall Matthews. 2000. The chinese value survey: An interpretation of value scales and consideration of some preliminary results. International Education Journal. [find](https://scholar.google.com/scholar?q=Barbara+Marshall+Matthews%2E+2000%2E+The+chinese+value+survey%3A+An+interpretation+of+value+scales+and+consideration+of+some+preliminary+results%2E+International+Education+Journal%2E)
24. Robin Hadwick. 2011. Should i use globe or hofstede? some insights that can assist cross-cultural scholars, and others, choose the right study to support their work. [find](https://scholar.google.com/scholar?q=Robin+Hadwick%2E+2011%2E+Should+i+use+globe+or+hofstede%3F+some+insights+that+can+assist+cross%2Dcultural+scholars%2C+and+others%2C+choose+the+right+study+to+support+their+work%2E)
25. Robert R McCrae, Antonio Terracciano, Anu Realo, and Jüri Allik. 2008. Interpreting globe societal practices scales. Journal of cross-cultural psychology, 39(6):805–810. [find](https://scholar.google.com/scholar?q=Robert+R+McCrae%2C+Antonio+Terracciano%2C+Anu+Realo%2C+and+J%C3%BCri+Allik%2E+2008%2E+Interpreting+globe+societal+practices+scales%2E+Journal+of+cross%2Dcultural+psychology%2C+39%286%29%3A805%E2%80%93810%2E)
26. G Hofstede and GJ Hofstede. 2022. [link]. [find](https://scholar.google.com/scholar?q=G+Hofstede+and+GJ+Hofstede%2E+2022%2E+%5Blink%5D%2E)
27. OpenAI. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774. [our card](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
28. Geert Hofstede. 2011. Dimensionalizing cultures: The hofstede model in context. Online readings in psychology and culture, 2(1):8. [find](https://scholar.google.com/scholar?q=Geert+Hofstede%2E+2011%2E+Dimensionalizing+cultures%3A+The+hofstede+model+in+context%2E+Online+readings+in+psychology+and+culture%2C+2%281%29%3A8%2E)
29. Geert Hofstede and Michael Minkov. 2019. [link]. [find](https://scholar.google.com/scholar?q=Geert+Hofstede+and+Michael+Minkov%2E+2019%2E+%5Blink%5D%2E)
30. G.J. Hofstede and M. Minkov. 2010. Cultures and Organizations: Software of the Mind, Third Edition. McGraw Hill LLC. [find](https://scholar.google.com/scholar?q=G%2EJ%2E+Hofstede+and+M%2E+Minkov%2E+2010%2E+Cultures+and+Organizations%3A+Software+of+the+Mind%2C+Third+Edition%2E+McGraw+Hill+LLC%2E)
31. Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019. The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751. [find](https://scholar.google.com/scholar?q=Ari+Holtzman%2C+Jan+Buys%2C+Li+Du%2C+Maxwell+Forbes%2C+and+Yejin+Choi%2E+2019%2E+The+curious+case+of+neural+text+degeneration%2E+arXiv+preprint+arXiv%3A1904%2E09751%2E)
32. Robert J House, Peter W Dorfman, Mansour Javidan, Paul J Hanges, and Mary F Sully De Luque. 2013. Strategic leadership across cultures: GLOBE study of CEO leadership behavior and effectiveness in 24 countries. Sage Publications. [find](https://scholar.google.com/scholar?q=Robert+J+House%2C+Peter+W+Dorfman%2C+Mansour+Javidan%2C+Paul+J+Hanges%2C+and+Mary+F+Sully+De+Luque%2E+2013%2E+Strategic+leadership+across+cultures%3A+GLOBE+study+of+CEO+leadership+behavior+and+effectiveness+in+24+c)
33. R. Inglehart, M. Basáñez, J. Díez-Medrano, L.C.J.M. Halman, and R. Luijkx, editors. 2004. Human beliefs and values: A cross-cultural sourcebook based on the 1999-2002 value surveys. Siglo XXI. Pagination: 520. [find](https://scholar.google.com/scholar?q=R%2E+Inglehart%2C+M%2E+Bas%C3%A1%C3%B1ez%2C+J%2E+D%C3%ADez%2DMedrano%2C+L%2EC%2EJ%2EM%2E+Halman%2C+and+R%2E+Luijkx%2C+editors%2E+2004%2E+Human+beliefs+and+values%3A+A+cross%2Dcultural+sourcebook+based+on+the+1999%2D2002+value+surveys%2E+Siglo+XXI%2E+Paginat)
34. Michael L Jones. 2007. Hofstede-culturally questionable? Oxford Business & Economics Conference. [find](https://scholar.google.com/scholar?q=Michael+L+Jones%2E+2007%2E+Hofstede%2Dculturally+questionable%3F+Oxford+Business+%26+Economics+Conference%2E)
35. Atoosa Kasirzadeh and Iason Gabriel. 2023. In conversation with artificial intelligence: aligning language models with human values. Philosophy & Technology, 36(2):1–24. [find](https://scholar.google.com/scholar?q=Atoosa+Kasirzadeh+and+Iason+Gabriel%2E+2023%2E+In+conversation+with+artificial+intelligence%3A+aligning+language+models+with+human+values%2E+Philosophy+%26+Technology%2C+36%282%29%3A1%E2%80%9324%2E)
36. Maurice G Kendall. 1938. A new measure of rank correlation. Biometrika, 30(1/2):81–93. [find](https://scholar.google.com/scholar?q=Maurice+G+Kendall%2E+1938%2E+A+new+measure+of+rank+correlation%2E+Biometrika%2C+30%281%2F2%29%3A81%E2%80%9393%2E)
37. Maurice G Kendall. 1945. The treatment of ties in ranking problems. Biometrika, 33(3):239–251. [find](https://scholar.google.com/scholar?q=Maurice+G+Kendall%2E+1945%2E+The+treatment+of+ties+in+ranking+problems%2E+Biometrika%2C+33%283%29%3A239%E2%80%93251%2E)
38. Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. 2022. Red teaming language models with language models. arXiv preprint arXiv:2202.03286. [our card](https://md.research.elamur.ai/free/en/papers/perez-2022-red-teaming-language-models-language/)
39. Vinodkumar Prabhakaran, Rida Qadri, and Ben Hutchinson. 2022. Cultural incongruencies in artificial intelligence. arXiv preprint arXiv:2211.13069. — [arXiv:2211.13069](https://arxiv.org/abs/2211.13069) [find](https://scholar.google.com/scholar?q=Vinodkumar+Prabhakaran%2C+Rida+Qadri%2C+and+Ben+Hutchinson%2E+2022%2E+Cultural+incongruencies+in+artificial+intelligence%2E+arXiv+preprint+arXiv%3A2211%2E13069%2E+%E2%80%94+%5BarXiv%3A2211%2E13069%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2211%2E13069)
40. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. The Journal of Machine Learning Research, 21(1):5485–5551. [our card](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
41. Paul Röttger, Hannah Rose Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, and Dirk Hovy. 2023. Xstest: A test suite for identifying exaggerated safety behaviours in large language models. arXiv preprint arXiv:2308.01263. [our card](https://md.research.elamur.ai/free/en/papers/rottger-2023-xstest-test-suite-identifying-exaggerated/)
42. Mustafa Safdari, Greg Serapio-García, Clément Crepy, Stephen Fitz, Peter Romero, Luning Sun, Marwa Abdulhai, Aleksandra Faust, and Maja Matarić. 2023. Personality traits in large language models. arXiv preprint arXiv:2307.00184. [our card](https://md.research.elamur.ai/free/en/papers/serapio-garcia-2023-personality-traits-large-language-models/)
43. Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto. 2023. Whose opinions do language models reflect? In Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pages 29971–30004. PMLR. [our card](https://md.research.elamur.ai/free/en/papers/santurkar-2023-whose-opinions-language-models-reflect/)
44. Guillaume Lample and Alexis Conneau. 2019. Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291. [find](https://scholar.google.com/scholar?q=Guillaume+Lample+and+Alexis+Conneau%2E+2019%2E+Cross%2Dlingual+language+model+pretraining%2E+arXiv+preprint+arXiv%3A1901%2E07291%2E)
45. Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. 2019. Social IQa: Commonsense reasoning about social interactions. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processi… [find](https://scholar.google.com/scholar?q=Maarten+Sap%2C+Hannah+Rashkin%2C+Derek+Chen%2C+Ronan+Le+Bras%2C+and+Yejin+Choi%2E+2019%2E+Social+IQa%3A+Commonsense+reasoning+about+social+interactions%2E+In+Proceedings+of+the+2019+Conference+on+Empirical+Methods+in)
46. Molly Lewis and Gary Lupyan. 2019. Gender stereotypes are reflected in the distributional structure of 25 languages. [find](https://scholar.google.com/scholar?q=Molly+Lewis+and+Gary+Lupyan%2E+2019%2E+Gender+stereotypes+are+reflected+in+the+distributional+structure+of+25+languages%2E)
47. Shalom Schwartz. 2012. An overview of the schwartz theory of basic values. Online Readings in Psychology and Culture, 2. [find](https://scholar.google.com/scholar?q=Shalom+Schwartz%2E+2012%2E+An+overview+of+the+schwartz+theory+of+basic+values%2E+Online+Readings+in+Psychology+and+Culture%2C+2%2E)
48. Kunal Khadilkar, Ashiqur R. KhudaBukhsh, and Tom M. Mitchell. 2022. Gender bias, social bias, and representation in bollywood and hollywood. Patterns, 3(2):100409. [find](https://scholar.google.com/scholar?q=Kunal+Khadilkar%2C+Ashiqur+R%2E+KhudaBukhsh%2C+and+Tom+M%2E+Mitchell%2E+2022%2E+Gender+bias%2C+social+bias%2C+and+representation+in+bollywood+and+hollywood%2E+Patterns%2C+3%282%29%3A100409%2E)
49. Hafiz Muhammad Abdullah Shaiq, Hafiz Muhammad Sufyan Khalid, Aisha Akram, and Bakhtiar Ali. 2011. Why not everybody loves hofstede? what are the alternative approaches to study of culture. European Journal of Business and Management, 3(6):101– 111. [find](https://scholar.google.com/scholar?q=Hafiz+Muhammad+Abdullah+Shaiq%2C+Hafiz+Muhammad+Sufyan+Khalid%2C+Aisha+Akram%2C+and+Bakhtiar+Ali%2E+2011%2E+Why+not+everybody+loves+hofstede%3F+what+are+the+alternative+approaches+to+study+of+culture%2E+European+Jo)
50. Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288. [find](https://scholar.google.com/scholar?q=Hugo+Touvron%2C+Louis+Martin%2C+Kevin+Stone%2C+Peter+Albert%2C+Amjad+Almahairi%2C+Yasmine+Babaei%2C+Nikolay+Bashlykov%2C+Soumya+Batra%2C+Prajjwal+Bhargava%2C+Shruti+Bhosale%2C+et+al%2E+2023%2E+Llama+2%3A+Open+foundation+and+fi)
51. Alexander Wei, Nika Haghtalab, and Jacob Steinhardt. 2023. Jailbroken: How does llm safety training fail? arXiv preprint arXiv:2307.02483. [our card](https://md.research.elamur.ai/free/en/papers/wei-2023-jailbroken-llm-safety-training-fail/)
52. Kuoshu Yang. 1987. Chinese values and the search for culture-free dimensions of culture. Journal of Cross-Cultural Psychology, 18(2):143–164. [find](https://scholar.google.com/scholar?q=Kuoshu+Yang%2E+1987%2E+Chinese+values+and+the+search+for+culture%2Dfree+dimensions+of+culture%2E+Journal+of+Cross%2DCultural+Psychology%2C+18%282%29%3A143%E2%80%93164%2E)
53. Boonghee Yoo, Naveen Donthu, and Tomasz Lenartow-icz. 2011. Measuring hofstede’s five dimensions of cultural values at the individual level: Development and validation of cvscale. Journal of international consumer marketing, 23(3-4):193–210. [find](https://scholar.google.com/scholar?q=Boonghee+Yoo%2C+Naveen+Donthu%2C+and+Tomasz+Lenartow%2Dicz%2E+2011%2E+Measuring+hofstede%E2%80%99s+five+dimensions+of+cultural+values+at+the+individual+level%3A+Development+and+validation+of+cvscale%2E+Journal+of+internati)
54. Xiang Yue, Boshi Wang, Kai Zhang, Ziru Chen, Yu Su, and Huan Sun. 2023. Automatic evaluation of attribution by large language models. arXiv preprint arXiv:2305.06311. [find](https://scholar.google.com/scholar?q=Xiang+Yue%2C+Boshi+Wang%2C+Kai+Zhang%2C+Ziru+Chen%2C+Yu+Su%2C+and+Huan+Sun%2E+2023%2E+Automatic+evaluation+of+attribution+by+large+language+models%2E+arXiv+preprint+arXiv%3A2305%2E06311%2E)

*Updated 24 October 2026*