Meta-Cultural Competence: Climbing the Right Hill of Cultural Awareness
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Abstract (arXiv)
Numerous recent studies have shown that Large Language Models (LLMs) are biased towards a Western and Anglo-centric worldview, which compromises their usefulness in non-Western cultural settings. However, "culture" is a complex, multifaceted topic, and its awareness, representation, and modeling in LLMs and LLM-based applications can be defined and measured in numerous ways. In this position paper, we ask what does it mean for an LLM to possess "cultural awareness", and through a thought experiment, which is an extension of the Octopus test proposed by Bender and Koller (2020), we argue that it is not cultural awareness or knowledge, rather meta-cultural competence, which is required of an LLM and LLM-based AI system that will make it useful across various, including completely unseen, cultures. We lay out the principles of meta-cultural competence AI systems, and discuss ways to measure and model those.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 52 of its 140 references that are in the map. First posted on arXiv 9 Feb 2025.
Builds on
52 of 140 references.
References · 52
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38 more
- 2023A Survey of Hallucination in Large Foundation Modelscited by 368
- 2024Break the Sequential Dependency of LLM Inference Using Lookahead Decodingcited by 298
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- 2023Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLUcited by 53
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50 most cited of 52 All 52 (JSON)
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Citing papers per year: 2025: 5; 2026: 3.
8 papers.
- 2025Fluent but Foreign: Even Regional LLMs Lack Cultural Alignmentcited by 10
- 2025Meaning Is Not A Metric: Using LLMs to make cultural context legible at scalecited by 10
- 2025When Cultures Meet: Multicultural Text-to-Image Generationcited by 7
- 2025Is It Bad to Work All the Time? Cross-Cultural Evaluation of Social Norm Biases in GPT-4cited by 5
- 2025Can LLMs Grasp Implicit Cultural Values? Benchmarking LLMs' Cultural Intelligence with CQ-Benchcited by 3
- 2026Defining Cultural Capabilities for AI Evaluation: A Taxonomy Grounded in Intercultural Communication Theorycited by 1
- 2026To Generate or Discriminate? Methodological Considerations for Measuring Cultural Alignment in LLMscited by 0
- 2026C-Mining: Unsupervised Discovery of Seeds for Cultural Data Synthesis via Geometric Misalignmentcited by 0
- 2026Defining Cultural Capabilities for AI Evaluation: A Taxonomy Grounded in Intercultural Communication Theory15 May 2026
- 2026C-Mining: Unsupervised Discovery of Seeds for Cultural Data Synthesis via Geometric Misalignment17 Apr 2026
- 2026To Generate or Discriminate? Methodological Considerations for Measuring Cultural Alignment in LLMs6 Jan 2026
- 2025Fluent but Foreign: Even Regional LLMs Lack Cultural Alignment25 May 2025
- 2025Meaning Is Not A Metric: Using LLMs to make cultural context legible at scale23 May 2025
- 2025Is It Bad to Work All the Time? Cross-Cultural Evaluation of Social Norm Biases in GPT-423 May 2025
- 2025Can LLMs Grasp Implicit Cultural Values? Benchmarking LLMs' Cultural Intelligence with CQ-Bench1 Apr 2025
- 2025When Cultures Meet: Multicultural Text-to-Image Generation21 Feb 2025