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Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models

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arxiv 2310.12481 v2 pith:KI3KUFKO submitted 2023-10-19 cs.CL cs.AI

Wenxuan Wang , Wenxiang Jiao , Jingyuan Huang , Ruyi Dai , Jen-tse Huang , Zhaopeng Tu , Michael R. Lyu This is my paper · ORCID
classification cs.CLcs.AI
keywords culturaldominanceissuellmsmodelsculturedatadeployment
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper identifies a cultural dominance issue within large language models (LLMs) due to the predominant use of English data in model training (e.g., ChatGPT). LLMs often provide inappropriate English-culture-related answers that are not relevant to the expected culture when users ask in non-English languages. To systematically evaluate the cultural dominance issue, we build a benchmark of concrete (e.g., holidays and songs) and abstract (e.g., values and opinions) cultural objects. Empirical results show that the representative GPT models suffer from the culture dominance problem, where GPT-4 is the most affected while text-davinci-003 suffers the least from this problem. Our study emphasizes the need to critically examine cultural dominance and ethical consideration in their development and deployment. We show that two straightforward methods in model development (i.e., pretraining on more diverse data) and deployment (e.g., culture-aware prompting) can significantly mitigate the cultural dominance issue in LLMs.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 7 citations worldwide. Full citation record

  1. MyCulture: Exploring Malaysia's Diverse Culture under Low-Resource Language Constraints

    cs.CL 2025-08 reject novelty 6.0 of 10

    MyCulture, a new Malay-language cultural benchmark, shows LLM accuracy drops by at least 17% when multiple-choice questions are converted to an open-ended format.

  2. CulFiT: A Fine-grained Cultural-aware LLM Training Paradigm via Multilingual Critique Data Synthesis

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A multilingual critique-data training paradigm with a knowledge-unit reward improves LLM cultural alignment on several benchmarks, but its headline benchmark is evaluated with the same LLM-judged metric used to select...

  3. CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries

    cs.AI 2025-01 conditional novelty 6.0 of 10

    CultureVerse is a 188-country, 19k-concept visual QA benchmark, and fine-tuning open VLMs on it improves cultural accuracy, but the main evaluation shares concepts between training and test sets.

  4. Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation

    cs.CL 2024-12 conditional novelty 6.0 of 10

    This paper quantifies Western-centric bias in MMLU, releases Global-MMLU across 42 languages with human-verified translations, and shows model rankings shift on culturally sensitive versus agnostic subsets.

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