大型语言模型低估且部分歪曲了日常规范中的文化差异

Computer Science > Computers and Society arXiv:2609.30896 (cs) [Submitted on 25 Sep 2026] Title:Large language models underestimate and partly misrepresent cultural variation in everyday norms Authors:Kimmo Eriksson, Irina Vartanova, Pontus Strimling View a PDF of the paper titled Large language models underestimate and partly misrepresent cultural variation in everyday norms, by Kimmo Eriksson and 2 other authors View PDF HTML (experimental) Abstract:A key aspect of culture is a society's norms about everyday behavior. How accurately do large language models (LLMs) represent cultural differences in such norms? To answer this question we used the recent Global Study of Everyday Norms (GSEN), which collected ratings of 150 scenarios in 90 societies, as the human benchmark. We prompted GPT-5 to estimate each society's average rating for every scenario, and later repeated the benchmark in three other LLMs: GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro. Compared to GSEN estimates, all four LLMs misrepresented cultural variation in two ways. First, they greatly underestimated its magnitude, estimating differences between societies to be, on average, less than half their measured size. Second, for many scenarios the LLMs poorly identified the pattern of variation, that is, which societies judged the behavior less acceptable and which societies judged it more acceptable. The pattern of variation was identified better for scenarios that elicit concerns about vulgarity, especially scenarios involving kissing and flirting. We also found that norms in more developed societies tended to be estimated somewhat more accurately, and that prompting in local survey languages rather than English produced only a modest improvement in accuracy. Local-language prompting also reduced, but did not remove, the underestimation of between-society differences. Cultural differences in everyday norms are only weakly and unevenly represented by LLMs. Subjects: Computers and Society (cs.CY) ACM classes: J.4; K.4.m; C.4 Cite as: arXiv:2609.30896 [cs.CY] (or arXiv:2609.30896v1 [cs.CY] for this version) https://doi.org/10.48550/arXiv.2609.30896 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Kimmo Eriksson [view email] [v1] Fri, 25 Sep 2026 07:04:13 UTC (1,308 KB) Full-text links: Access Paper: View a PDF of the paper titled Large language models underestimate and partly misrepresent cultural variation in everyday norms, by Kimmo Eriksson and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CY new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)