ArticlePNAS nexus2024
Cultural bias and cultural alignment of large language models.
Article in PNAS nexus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Cultural Prompting Improves the Empathy and Cultural Responsiveness of GPT-Generated Therapy Responses.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Trial
- Language and cultural prompts influence large language models' responses to culturally anchored mental health attitudes.PEC innovation · 2026Article
- Evaluation of an Artificial Intelligence-Driven Language Model for Age-Specific Patient Education in Pediatric Orthopaedics.Journal of the Pediatric Orthopaedic Society of North America · 2026Article
- Article
- Large Language Model Responses to Common Otolaryngological Questions: Evaluating Accuracy, Appropriateness, Readability, and Hallucinations.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Observational
- How large language models judge and influence human cooperation.PNAS nexus · 2026Article
- AI-Enabled Real-World Evidence in Oncology: A Statistical Perspective for Regulatory Decisions.Therapeutic innovation & regulatory science · 2026Review
- Article
- AI Mental Health in Asia: Cultural Misalignment, Clinical Risks and the Critical Role of Psychiatric Nursing.International journal of mental health nursing · 2026Article
- Beyond transparency: why Traditional Chinese Medicine (TCM) need explainable artificial intelligence (XAI).Chinese medicine · 2026Review
- Comparing human and AI emotional evaluations of images: GPT-4o performance across standard, persona, and language-specific prompting strategies.BMC psychology · 2026Article
- Recognising and mitigating LLM Pollution in online behavioural research.Nature communications · 2026Article
- Culturally-Aware Prompting in conversational AI: supporting perceived communicative effectiveness in cross-cultural teams in Australia.Scientific reports · 2026Article
- Evaluating large language models on multilingual vaccine knowledge: a benchmark study.NPJ vaccines · 2026Article
- Evaluating the Quality of Health Information: Comparison of Human and Artificial Intelligence.Neurogastroenterology and motility · 2026Article
- Linguistic Comparison of AI- and Human-Written Responses to Online Mental Health Queries.NPJ artificial intelligence · 2026Article
- Large language models identify immigration attitudes in online discourse regardless of language.Scientific reports · 2026Article
- Visual Moral Inference and Communication.Topics in cognitive science · 2026Article
- A comparison of large language model-generated and published perioperative neurocognitive disorder recommendations: a cross-sectional web-based analysis.British journal of anaesthesia · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 later 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.