Cultural Bias and Cultural Alignment of Large Language Models
On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment
Abstract (arXiv)
Culture fundamentally shapes people's reasoning, behavior, and communication. As people increasingly use generative artificial intelligence (AI) to expedite and automate personal and professional tasks, cultural values embedded in AI models may bias people's authentic expression and contribute to the dominance of certain cultures. We conduct a disaggregated evaluation of cultural bias for five widely used large language models (OpenAI's GPT-4o/4-turbo/4/3.5-turbo/3) by comparing the models' responses to nationally representative survey data. All models exhibit cultural values resembling English-speaking and Protestant European countries. We test cultural prompting as a control strategy to increase cultural alignment for each country/territory. For recent models (GPT-4, 4-turbo, 4o), this improves the cultural alignment of the models' output for 71-81% of countries and territories. We suggest using cultural prompting and ongoing evaluation to reduce cultural bias in the output of generative AI.
Key facts. Cited by 149 papers in the map (data through 14 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 10 of its 51 references that are in the map. First posted on arXiv 23 Nov 2023.
Builds on
10 of 51 references.
References · 10
- 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,312
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- 2022The Ghost in the Machine has an American accent: value conflict in GPT-3cited by 105
- 2023Knowledge of cultural moral norms in large language modelscited by 51
- 2021Designing Disaggregated Evaluations of AI Systems: Choices, Considerations, and Tradeoffscited by 48
- 2021Artificial intelligence in communication impacts language and social relationshipscited by 22
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Citing papers per year: 2024: 29; 2025: 57; 2026: 63.
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