[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83951-en":3,"doc-seo-83951-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83951,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","CCBENCH Assessing LLM Cultural Competence via Implicitly Signaled Norms using Health Queries","To ensure AI interactions remain fair and non-stereotyping, cultural competency is required: the ability to infer and adapt to users’ implicitly signaled cultural values rather than relying on fixed demographic traits. This work presents CCBENCH, a benchmark framework that models culture as a continuum of norm-adherence states. CCBENCH-Health instantiates the framework with 60 grounded personas, 6 cultures, and 52 real health queries, totaling 3,120 interactions across five leading LLMs.","arXiv :2607 .05405v 1 [ cs .CY] 8 Jun 2026  \nCCBENCH: Assessing LLM Cultural Competence via Implicitly Signaled Norms using Health Queries  \nVasudha Varadarajan, Akhila Yerukola, Mona T. Diab & Maarten Sap  \nCarnegie Mellon University Pittsburgh, PA 15213, USA  \n{vvaradar, ayerukola, mdiab, [msap2](msap2}@andrew.cmu.edu)[}](msap2}@andrew.cmu.edu)[@andrew.cmu.edu](msap2}@andrew.cmu.edu)  \nAbstract  \nTo interact with users fairly and without stereotyping, AI models must display cultural competency, i.e., the ability to infer and adapt to a user’s implicitly signaled cultural values, rather than relying on static demographic traits.  \nWe introduce CCBENCH, a framework for evaluating cultural competency in large language models (LLMs), treating culture as a continuum of norm adherence states rather than as a binary state of cultural belongingness.  \nAs a case study on health, we create CCBENCH-Health, which includes 60 theoretically grounded personas exhibiting varied norm-adherence states across six cultures, each engaging in 18 realistic dialogues. Each persona is evaluated on 52 authentic healthcare questions drawn from real user forums, yielding 3,120 unique interactions. Benchmarking five leading models reveals that even the best achieve culturally appropriate responses only 20-30% of the time. When explicitly prompted to focus on culturally relevant cues from the conversational history (CoT), performance improves modestly by 3-5% on average. We find that models perform best when personas avoid cultural norms rather than follow them, revealing a persistent asymmetry, suggesting a preference in the models to align with built-in biases than adapt to cultural cues. This is especially observed in the Afghan context (Avg: 8.8%), where cultural cues rarely yield appropriate health advice. Finally, we find that models sometimes adapt more readily to implicit, cultural conversational styles than to explicitly stated cultural practices, though this varies across cultures.  \n1 Introduction  \nLarge Language Models (LLMs) are now global intermediaries of information that interact with a diverse global user base; however, they frequently default to ”WEIRD”(Western, Educated, Industrialized, Rich, Democratic) value systems, leading to a persistent cultural bias (Ryan et al., 2024; Jiang et al., 2024; Mire et al., 2025) . To address this gap, we draw on cultural competence – a concept originally developed in healthcare to describe practitioners’ability to provide effective care to patients with diverse values and beliefs (Cross, 2013; Aleem et al., 2024) . In high-stakes domains like health, where content and communication style can directly impact user engagement, a model’s inability to align with a user’s cultural expectations can lead to fundamental erosion of trust and safety (Schmutz et al., 2024; Orrall & Rekito, 2025) .  \nA significant challenge in achieving this competence in AI is that cultural belonging and norm adherence are rarely made explicit in natural interaction. Users seldom declare their heritage or belief systems directly; instead, they signal their context through implicit narratives, communication preferences, and social cues. As LLMs become increasingly personalized and relied upon for high-stakes decisions (Sun & Zhou, 2023; Cheung et al., 2025), they must develop sensitivity to recognize these subtle signals. However, current benchmarks largely treat cultural identity as a binary attribute—categorizing a user as either belonging to a culture or not (Chiu et al., 2025; Li et al., 2024a) . This reductive  \napproach ignores the significant intracultural variation and fluidity of real human identity. A model that assumes every user from a specific background adheres monolithically to their traditional norms risks not only misalignment but also the reinforcement of harmful stereotypes (Baumert et al., 2024; Khan et al., 2025) .  \nWe introduce CCBENCH, a framework designed to evaluate cultural competence in LLMs. Our fram","cbCaik8aBw1Km6lN","https://ap.wps.com/l/cbCaik8aBw1Km6lN","pdf",2380713,1,34,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What does cultural competency mean for large language models in this paper?\",\"answer\":\"Cultural competency refers to a model’s ability to infer and adapt to a user’s implicitly signaled cultural values, rather than treating culture as a static binary demographic attribute.\"},{\"question\":\"How does CCBENCH evaluate cultural competence?\",\"answer\":\"CCBENCH treats culture as a continuum of norm-adherence states and uses simulated conversational histories to implicitly reveal a persona’s position on that spectrum, then checks whether models infer it and calibrate responses.\"},{\"question\":\"What is the key finding from CCBENCH-Health about model performance?\",\"answer\":\"Even the best models provide culturally appropriate responses only about 20–30% of the time, with modest improvement (3–5%) when prompted to focus on culturally relevant cues.\"}]",1784191646,86,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"ccbench-assessing-llm-cultural-competence-via-implicitly-signaled-norms-using-health-queries","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/ccbench-assessing-llm-cultural-competence-via-implicitly-signaled-norms-using-health-queries/83951/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-26","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does cultural competency mean for large language models in this paper?","Question",{"text":75,"@type":76},"Cultural competency refers to a model’s ability to infer and adapt to a user’s implicitly signaled cultural values, rather than treating culture as a static binary demographic attribute.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does CCBENCH evaluate cultural competence?",{"text":80,"@type":76},"CCBENCH treats culture as a continuum of norm-adherence states and uses simulated conversational histories to implicitly reveal a persona’s position on that spectrum, then checks whether models infer it and calibrate responses.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the key finding from CCBENCH-Health about model performance?",{"text":84,"@type":76},"Even the best models provide culturally appropriate responses only about 20–30% of the time, with modest improvement (3–5%) when prompted to focus on culturally relevant 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