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CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting

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arxiv 2404.10199 v5 pith:GI24QMQA submitted 2024-04-16 cs.CL cs.AI

CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting

Huihan Li , Liwei Jiang , Jena D. Hwang , Hyunwoo Kim , Sebastin Santy , Taylor Sorensen , Bill Yuchen Lin , Nouha Dziri
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Xiang Ren Yejin Choi
This is my paper
classification cs.CL cs.AI
keywords cultureculturesllmsgloballanguagemodelsculture-conditionedculture-gen
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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As the utilization of large language models (LLMs) has proliferated world-wide, it is crucial for them to have adequate knowledge and fair representation for diverse global cultures. In this work, we uncover culture perceptions of three SOTA models on 110 countries and regions on 8 culture-related topics through culture-conditioned generations, and extract symbols from these generations that are associated to each culture by the LLM. We discover that culture-conditioned generation consist of linguistic "markers" that distinguish marginalized cultures apart from default cultures. We also discover that LLMs have an uneven degree of diversity in the culture symbols, and that cultures from different geographic regions have different presence in LLMs' culture-agnostic generation. Our findings promote further research in studying the knowledge and fairness of global culture perception in LLMs. Code and Data can be found here: https://github.com/huihanlhh/Culture-Gen/

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Cited by 4 Pith papers

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

  1. MET: Theory-Grounded and Culture-Aware Multilingual Moral Reasoning

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    MET-D self-distills theory-selected moral grounds into native-language reasoning, lifting macro-F1 by ~3.7–4.2 points on MCLASH and MMoralExceptQA while raising native-language chains by ~62 points.

  2. When Cultures Move: Measuring and Improving Multicultural Text-to-Video Generation

    cs.CV 2026-05 unverdicted novelty 6.0

    MAVEN is a multi-agent prompt refinement framework that improves cultural fidelity in text-to-video generation, demonstrated on a new benchmark of 243 prompts and 972 videos across Chinese, American, and Romanian cultures.

  3. When Cultures Move: Measuring and Improving Multicultural Text-to-Video Generation

    cs.CV 2026-05 unverdicted novelty 6.0

    MAVEN introduces a multi-agent system for refining prompts in multicultural text-to-video generation and releases a benchmark of 243 prompts and 972 videos showing improved cultural relevance via parallel agent specia...

  4. Representational Harms in LLM-Generated Narratives Against Global Majority Nationalities

    cs.CL 2026-04 unverdicted novelty 5.0

    LLMs generate narratives containing persistent stereotypes, erasure, and one-dimensional portrayals of Global Majority national identities, with minoritized groups overrepresented in subordinated roles by more than fi...