Large language models can replicate cross-cultural differences in personality
Not on the map. Why?
Abstract (arXiv)
We use a large-scale experiment (N=8000) to determine whether GPT-4 can replicate cross-cultural differences in the Big Five, measured using the Ten-Item Personality Inventory. We used the US and South Korea as the cultural pair, given that prior research suggests substantial personality differences between people from these two countries. We manipulated the target of the simulation (US vs. Korean), the language of the inventory (English vs. Korean), and the language model (GPT-4 vs. GPT-3.5). Our results show that GPT-4 replicated the cross-cultural differences for each factor. However, mean ratings had an upward bias and exhibited lower variation than in the human samples, as well as lower structural validity. We provide preliminary evidence that LLMs can aid cross-cultural researchers and practitioners.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 8 of its 47 references that are in the map. First posted on arXiv 12 Oct 2023.
Builds on
8 of 47 references.
References · 8
- 2017Attention Is All You Needcited by 59,932
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
- 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
- 2017Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraintscited by 420
- 2018Women also Snowboard: Overcoming Bias in Captioning Modelscited by 236
- 2023Personality Traits in Large Language Modelscited by 166
- 2022Who is GPT-3? An Exploration of Personality, Values and Demographicscited by 112
- 2022Deep Lexical Hypothesis: Identifying personality structure in natural languagecited by 10
Built on by
Citing papers per year: 2024: 3; 2025: 2; 2026: 3.
8 papers.
- 2024CultureLLM: Incorporating Cultural Differences into Large Language Modelscited by 49
- 2024LLM-GLOBE: A Benchmark Evaluating the Cultural Values Embedded in LLM Outputcited by 27
- 2025Large Language Models as Psychological Simulators: A Methodological Guidecited by 6
- 2025Through the Prism of Culture: Evaluating LLMs' Understanding of Indian Subcultures and Traditionscited by 3
- 2026Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Modelscited by 3
- 2024Communicate to Play: Pragmatic Reasoning for Efficient Cross-Cultural Communication in Codenamescited by 2
- 2026DVMap: Fine-Grained Pluralistic Value Alignment via High-Consensus Demographic-Value Mappingcited by 1
- 2026Anchoring LLM Gender Bias to Human Baselines: A Cross-Lingual Auditcited by 0
- 2026Anchoring LLM Gender Bias to Human Baselines: A Cross-Lingual Audit29 May 2026
- 2026DVMap: Fine-Grained Pluralistic Value Alignment via High-Consensus Demographic-Value Mapping14 May 2026
- 2026Mind the Gap in Cultural Alignment: Task-Aware Culture Management for Large Language Models25 Feb 2026
- 2025Large Language Models as Psychological Simulators: A Methodological Guide20 Jun 2025
- 2025Through the Prism of Culture: Evaluating LLMs' Understanding of Indian Subcultures and Traditions28 Jan 2025
- 2024LLM-GLOBE: A Benchmark Evaluating the Cultural Values Embedded in LLM Output9 Nov 2024
- 2024Communicate to Play: Pragmatic Reasoning for Efficient Cross-Cultural Communication in Codenames9 Aug 2024
- 2024CultureLLM: Incorporating Cultural Differences into Large Language Models9 Feb 2024