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Evaluating Cultural Awareness of LLMs for Yoruba, Malayalam, and English

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arxiv 2410.01811 v1 pith:D4BPQ6RF submitted 2024-09-14 cs.CY cs.AIcs.CL

classification cs.CYcs.AIcs.CL
keywords culturalllmsmalayalamregionalyorubaalthoughawarenessenglish
verification ladder T0 review T1 audit T2 compute T3 formal
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Although LLMs have been extremely effective in a large number of complex tasks, their understanding and functionality for regional languages and cultures are not well studied. In this paper, we explore the ability of various LLMs to comprehend the cultural aspects of two regional languages: Malayalam (state of Kerala, India) and Yoruba (West Africa). Using Hofstede's six cultural dimensions: Power Distance (PDI), Individualism (IDV), Motivation towards Achievement and Success (MAS), Uncertainty Avoidance (UAV), Long Term Orientation (LTO), and Indulgence (IVR), we quantify the cultural awareness of LLM-based responses. We demonstrate that although LLMs show a high cultural similarity for English, they fail to capture the cultural nuances across these 6 metrics for Malayalam and Yoruba. We also highlight the need for large-scale regional language LLM training with culturally enriched datasets. This will have huge implications for enhancing the user experience of chat-based LLMs and also improving the validity of large-scale LLM agent-based market research.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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

    cs.CY 2025-02 conditional novelty 6.0 of 10

    The paper argues that LLMs should be evaluated and built for meta-cultural competence rather than static knowledge of specific cultures, and gives a first, illustrative measurement of one component.

  2. Reading between the Lines: Can LLMs Identify Cross-Cultural Communication Gaps?

    cs.CL 2025-02 conditional novelty 6.0 of 10

    A user study of 57 preselected Goodreads reviews finds culture-specific comprehension gaps in most texts, while GPT-4o identifies the relevant spans with 0.49 precision and 0.65 recall across India, Mexico, and the USA.

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