mlsanityData to 14 Oct 2026

Meta-Cultural Competence: Climbing the Right Hill of Cultural Awareness

arXiv 2502.09637 ↗First posted 9 Feb 2025cs.CY cs.AI cs.CLcited by 8

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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
  1. 2023A Survey of Hallucination in Large Foundation Modelscited by 368
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  18. 2024How Well Do LLMs Represent Values Across Cultures? Empirical Analysis of LLM Responses Based on Hofstede Cultural Dimensionscited by 52
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.