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Cited by 191Topic ModelingEthics and Social Impacts of AINatural Language Processing Techniques

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Having Beer after Prayer? Measuring Cultural Bias in Large Language Models

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Naous, Tarek, Ryan, Michael J, Ritter, Alan, Xu, Wei·2024·Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
DOI 10.18653/v1/2024.acl-long.862
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Abstract

As the reach of large language models (LMs) expands globally, their ability to cater to diverse cultural contexts becomes crucial.Despite advancements in multilingual capabilities, models are not designed with appropriate cultural nuances.In this paper, we show that multilingual and Arabic monolingual LMs exhibit bias towards entities associated with Western culture.We introduce CAMeL, a novel resource of 628 naturally-occurring prompts and 20,368 entities spanning eight types that contrast Arab and Western cultures.CAMeL provides a foundation for measuring cultural biases in LMs through both extrinsic and intrinsic evaluations.Using CAMeL, we examine the cross-cultural performance in Arabic of 16 different LMs on tasks such as story generation, NER, and sentiment analysis, where we find concerning cases of stereotyping and cultural unfairness.We further test their text-infilling performance, revealing the incapability of appropriate adaptation to Arab cultural contexts.Finally, we analyze 6 Arabic pre-training corpora and find that commonly used sources such as Wikipedia may not be best suited to build culturally aware LMs, if used as they are without adjustment.We will make CAMeL publicly available at: https://github.com/tareknaous/camel

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Metadata

Authors
Naous, T., Ryan, M. J., Ritter, A., & Xu, W.
Affiliations
Georgia Institute of Technology (US)
ORCID
Venue
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher
Association for Computational Linguistics
Published
2024
Pages
16366-16393
ISSN
0736-587X (ISSN-L)
DOI
10.18653/v1/2024.acl-long.862
OpenAlex
W4402671169
Cited by
96
References
0
Citations by year
2026: 40 · 2025: 42 · 2024: 10
FWCI
27.15
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Funders
  • National Science Foundation (2052498, 2144493)
  • Intelligence Advanced Research Projects Activity
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
Keywords
Arabic language models, cross-cultural assessment, text generation, named entity recognition, sentiment analysis, text infilling, cultural fairness

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