Culture is Everywhere: A Call for Intentionally Cultural Evaluation
On the map: LLM alignment / RLHF / preference optimisation → Human values and AI feedback alignment
Abstract (arXiv)
The prevailing ``trivia-centered paradigm'' for evaluating the cultural alignment of large language models (LLMs) is increasingly inadequate as these models become more advanced and widely deployed. Existing approaches typically reduce culture to static facts or values, testing models via multiple-choice or short-answer questions that treat culture as isolated trivia. Such methods neglect the pluralistic and interactive realities of culture, and overlook how cultural assumptions permeate even ostensibly ``neutral'' evaluation settings. In this position paper, we argue for intentionally cultural evaluation: an approach that systematically examines the cultural assumptions embedded in all aspects of evaluation, not just in explicitly cultural tasks. We systematically characterize the what, how, and circumstances by which culturally contingent considerations arise in evaluation, and emphasize the importance of researcher positionality for fostering inclusive, culturally aligned NLP research. Finally, we discuss implications and future directions for moving beyond current benchmarking practices, discovering important applications that we don't know exist, and involving communities in evaluation design through HCI-inspired participatory methodologies.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Placed in Human values and AI feedback alignment, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 40 of its 74 references that are in the map. First posted on arXiv 1 Sep 2025.
Builds on
40 of 74 references.
Closest ancestors · 1
Same direction, newest first
Other references · 39
- 2020Measuring Massive Multitask Language Understandingcited by 7,963
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
- 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,899
- 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 1,006
- 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 956
- 2024WildChat: 1M ChatGPT Interaction Logs in the Wildcited by 629
- 2023PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Promptscited by 226
- 2024BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languagescited by 173
- 2024Towards Measuring and Modeling "Culture" in LLMs: A Surveycited by 167
- 2024ProSA: Assessing and Understanding the Prompt Sensitivity of LLMscited by 149
- 2022The Ghost in the Machine has an American accent: value conflict in GPT-3cited by 105
- 2024Survey of Cultural Awareness in Language Models: Text and Beyondcited by 103
27 more
- 2024The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performancecited by 100
- 2023Better to Ask in English: Cross-Lingual Evaluation of Large Language Models for Healthcare Queriescited by 82
- 2024Understanding and Mitigating Language Confusion in LLMscited by 75
- 2024Clio: Privacy-Preserving Insights into Real-World AI Usecited by 72
- 2023KoBBQ: Korean Bias Benchmark for Question Answeringcited by 71
- 2019How should my chatbot interact? A survey on human-chatbot interaction designcited by 56
- 2022Re-contextualizing Fairness in NLP: The Case of Indiacited by 55
- 2025Randomness, Not Representation: The Unreliability of Evaluating Cultural Alignment in LLMscited by 54
- 2023Exploring Cross-Cultural Differences in English Hate Speech Annotations: From Dataset Construction to Analysiscited by 45
- 2024Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluationcited by 41
- 2025Culture is Not Trivia: Sociocultural Theory for Cultural NLPcited by 40
- 2024Benchmarks as Microscopes: A Call for Model Metrologycited by 35
- 2024CaLMQA: Exploring culturally specific long-form question answering across 23 languagescited by 31
- 2023X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspectscited by 28
- 2024"They are uncultured": Unveiling Covert Harms and Social Threats in LLM Generated Conversationscited by 25
- 2024Extrinsic Evaluation of Cultural Competence in Large Language Modelscited by 24
- 2024How Culture Shapes What People Want From AIcited by 23
- 2024Cultural Conditioning or Placebo? On the Effectiveness of Socio-Demographic Promptingcited by 22
- 2024Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScorecited by 18
- 2025The Case for "Thick Evaluations" of Cultural Representation in AIcited by 17
- 2025The Value of Disagreement in AI Design, Evaluation, and Alignmentcited by 12
- 2025Lost in the Mix: Evaluating LLM Understanding of Code-Switched Textcited by 10
- 2024ChatGPT Doesn't Trust Chargers Fans: Guardrail Sensitivity in Contextcited by 9
- 2024Exploring Empty Spaces: Human-in-the-Loop Data Augmentationcited by 6
- 2020The Effect of Sociocultural Variables on Sarcasm Communication Onlinecited by 5
- 2024Examining Human-AI Collaboration for Co-Writing Constructive Comments Onlinecited by 3
- 2023Understanding the Impact of Culture in Assessing Helpfulness of Online Reviewscited by 1
Built on by
Citing papers per year: 2026: 10.
10 papers.
- 2026Tiny Aya: Bridging Scale and Multilingual Depthcited by 15
- 2026A Unified Framework to Quantify Cultural Intelligence of AIcited by 2
- 2026Lessons Without Borders? Evaluating Cultural Alignment of LLMs Using Multilingual Story Moral Generationcited by 2
- 2026The Culture Funnel: You Can't Align What isn't in the Datacited by 2
- 2026Benchmarking Frontier LLMs on Arabic Cultural and Sociolinguistic Knowledge: A Cross-Evaluation Framework with Human SME Ground Truthcited by 1
- 2026CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapterscited by 0
- 2026Toward Culturally Grounded Natural Language Processingcited by 0
- 2026JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errorscited by 0
- 2026Occupational Prompting Reveals Cultural Bias in Large Language Modelscited by 0
- 2026When Trivia Is Not Trivial: Everyday Knowledge Failures in Multilingual LLMscited by 0
- 2026When Trivia Is Not Trivial: Everyday Knowledge Failures in Multilingual LLMs23 Jul 2026
- 2026Benchmarking Frontier LLMs on Arabic Cultural and Sociolinguistic Knowledge: A Cross-Evaluation Framework with Human SME Ground Truth30 Jun 2026
- 2026The Culture Funnel: You Can't Align What isn't in the Data11 Jun 2026
- 2026JuICE: A Benchmark for Evaluating LLM-Judge in Identifying Cultural Errors26 May 2026
- 2026Occupational Prompting Reveals Cultural Bias in Large Language Models19 May 2026
- 2026Lessons Without Borders? Evaluating Cultural Alignment of LLMs Using Multilingual Story Moral Generation9 Apr 2026
- 2026Toward Culturally Grounded Natural Language Processing27 Mar 2026
- 2026Tiny Aya: Bridging Scale and Multilingual Depth12 Mar 2026
- 2026A Unified Framework to Quantify Cultural Intelligence of AI1 Mar 2026
- 2026CuMA: Aligning LLMs with Sparse Cultural Values via Demographic-Aware Mixture of Adapters8 Jan 2026
Similar, not cited
Close in wording, no citation either way
- 2025CURE: Cultural Understanding and Reasoning Evaluation - A Framework for "Thick" Culture Alignment Evaluation in LLMs
- 2025Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs
- 2023CDEval: A Benchmark for Measuring the Cultural Dimensions of Large Language Models
- 2025From Word to World: Evaluate and Mitigate Culture Bias in LLMs via Word Association Test
- 2026Aligned but Flattened: Analyzing the Trade-off between Cultural Alignment and Diversity in LLMs
- 2025CultureSynth: A Hierarchical Taxonomy-Guided and Retrieval-Augmented Framework for Cultural Question-Answer Synthesis
- 2025CulFiT: A Fine-grained Cultural-aware LLM Training Paradigm via Multilingual Critique Data Synthesis
- 2024Self-Pluralising Culture Alignment for Large Language Models