Is It Bad to Work All the Time? Cross-Cultural Evaluation of Social Norm Biases in GPT-4
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Abstract (arXiv)
LLMs have been demonstrated to align with the values of Western or North American cultures. Prior work predominantly showed this effect through leveraging surveys that directly ask (originally people and now also LLMs) about their values. However, it is hard to believe that LLMs would consistently apply those values in real-world scenarios. To address that, we take a bottom-up approach, asking LLMs to reason about cultural norms in narratives from different cultures. We find that GPT-4 tends to generate norms that, while not necessarily incorrect, are significantly less culture-specific. In addition, while it avoids overtly generating stereotypes, the stereotypical representations of certain cultures are merely hidden rather than suppressed in the model, and such stereotypes can be easily recovered. Addressing these challenges is a crucial step towards developing LLMs that fairly serve their diverse user base.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 25 of its 55 references that are in the map. First posted on arXiv 23 May 2025.
Builds on
25 of 55 references.
References · 25
- 2023GPT-4 Technical Reportcited by 18,396
- 2020StereoSet: Measuring stereotypical bias in pretrained language modelscited by 924
- 2016Semantics derived automatically from language corpora contain human-like biasescited by 901
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
- 2023Large Language Modelscited by 780
- 2023Whose Opinions Do Language Models Reflect?cited by 773
- 2023Towards Measuring the Representation of Subjective Global Opinions in Language Modelscited by 437
- 2022ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detectioncited by 422
- 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
- 2020Social Chemistry 101: Learning to Reason about Social and Moral Normscited by 255
- 2022Challenges and Strategies in Cross-Cultural NLPcited by 218
- 2023Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Modelscited by 207
13 more
- 2023Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Studycited by 206
- 2023The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientationcited by 189
- 2022Probing Pre-Trained Language Models for Cross-Cultural Differences in Valuescited by 149
- 2023Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensionscited by 112
- 2022The Ghost in the Machine has an American accent: value conflict in GPT-3cited by 105
- 2023SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Modelscited by 66
- 2023Multilingual Language Models are not Multicultural: A Case Study in Emotioncited by 54
- 2024Evaluating Cultural and Social Awareness of LLM Web Agentscited by 45
- 2025Culture is Not Trivia: Sociocultural Theory for Cultural NLPcited by 40
- 2023Mitigating Test-Time Bias for Fair Image Retrievalcited by 20
- 2025Meta-Cultural Competence: Climbing the Right Hill of Cultural Awarenesscited by 8
- 2024Can I introduce my boyfriend to my grandmother? Evaluating Large Language Models Capabilities on Iranian Social Norm Classificationcited by 7
- 2023I'm Afraid I Can't Do That: Predicting Prompt Refusal in Black-Box Generative Language Modelscited by 6
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Citing papers per year: 2026: 5.
5 papers.
- 2026InfoGatherer: Principled Information Seeking via Evidence Retrieval and Strategic Questioningcited by 3
- 2026Every Token Counts: Exact Likert-Scale Distributions for Measuring LLM Attitudes and Biasescited by 3
- 2026Representational Harms in LLM-Generated Narratives Against Global Majority Nationalitiescited by 2
- 2026AI as a Tool for Simulation-Based Experiments in Literary Studiescited by 2
- 2026LLMs Infer Cultural Context but Fail to Apply It When Respondingcited by 0
- 2026Every Token Counts: Exact Likert-Scale Distributions for Measuring LLM Attitudes and Biases11 Aug 2026
- 2026LLMs Infer Cultural Context but Fail to Apply It When Responding16 Jun 2026
- 2026AI as a Tool for Simulation-Based Experiments in Literary Studies1 Jun 2026
- 2026Representational Harms in LLM-Generated Narratives Against Global Majority Nationalities24 Apr 2026
- 2026InfoGatherer: Principled Information Seeking via Evidence Retrieval and Strategic Questioning6 Mar 2026