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home / papers / 2023 / Having Beer after Prayer? Measuring Cultural Bias in Large Language Models

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

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2305.14456

Summary

Large language models are deployed worldwide, yet even in Arabic they often fall back on Western cultural content. For example, they suggest alcoholic drinks to finish a sentence about meeting friends after Islamic prayer. The authors test whether multilingual and Arabic-specific LMs systematically favour Western entities, and build CAMeL (Cultural Appropriateness Measure Set for LMs) for this purpose. CAMeL combines 20,368 Arab and Western entities in eight categories with 628 masked prompts adapted from real Twitter/X posts. The entities were collected from Wikidata and by pattern matching on Arabic CommonCrawl, then manually checked. Using CAMeL, the authors evaluate 16 LMs trained with Arabic data on four tasks: generating stories, recognising named entities (NER), classifying sentiment, and filling in masked text. The models show three problems:

  • stereotypes, such as linking Arab names with poverty and traditionalism;
  • unfair task behaviour, including weaker NER on Arab entities and more negative sentiment attached to them;
  • a preference for Western entities even in clearly Arab contexts.

An analysis of six Arabic pre-training corpora suggests that sources like Wikipedia feed this bias. The work offers a reusable benchmark for culturally aware NLP beyond English.

Key points

  • CAMeL covers eight entity types: person names, food dishes, beverages, clothing, cities, literary authors, places of worship and sports clubs. It contains 20,368 entities and 628 prompts, and all of it is in Arabic.
  • Entity extraction deliberately avoids LMs. Wikidata classes are used where Arabic coverage is good (locations, authors, sports clubs). Elsewhere, 5–10 hand-written patterns per type are applied to CommonCrawl.
  • Prompts are anonymised: the entities in the original tweets are replaced with [MASK]. Raw tweets are not released.
  • Corpus analysis trains n-gram LMs on each of six Arabic pre-training corpora and compares their infilling on CAMeL. Local news and Twitter/X show the least Western bias, while Wikipedia is less suitable without adjustment.
  • The dataset is published at github.com/tareknaous/camel. Stated limitations are that it covers Arabic only, uses a binary Arab/Western split, and analyses stereotypes only through adjectives.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Topics: Named entity recognition

Cited by

178 papers of our library cite it (of the 140673 whose bibliographies we read); 5 of them have a page here, 173 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2305.14456 · arXiv 2305.14456

@misc{naous2023having,
  title = {{Having Beer after Prayer? Measuring Cultural Bias in Large Language Models}},
  author = {Naous, Tarek and Ryan, Michael J. and Ritter, Alan and Xu, Wei},
  year = {2023},
  doi = {10.48550/arxiv.2305.14456},
  eprint = {2305.14456},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2305.14456}
}

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