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LLM Alignment for the Arabs: A Homogenous Culture or Diverse Ones

Paper · 2025 · Amr Keleg

Summary

This position paper challenges a widespread assumption in Arabic NLP: that everyone who speaks Arabic shares a single culture. The author contrasts two extremes. One is a unified pan-Arab view, supported by Arab nationalism and Islam. The other treats each nation as a distinct culture rooted in its own heritage. The paper then surveys how culture appears in Arabic resources:

  • Task datasets: dialectal samples are often given to annotators who do not speak that dialect.
  • Culture benchmarks: these are mostly curated country by country.
  • Alignment and instruction data: "correct" answers often reflect only some regions, typically the Gulf.

Arabic-centric LLMs such as AceGPT, ALLaM and Fanar are promoted as aligned with "Arab culture," yet this target is rarely defined. The author proposes a research agenda:

  • study what speakers in different regions actually need, drawing on anthropology and HCI;
  • find out which variety they prefer for interacting with technology (MSA, a local dialect, English or French);
  • collect alignment data that is regionally diverse.

The point matters for any language community where one label hides many cultures.

Key points

  • PRISM had 1,500 participants, but only 51 lived in the Middle East, 47 of them in Israel. Only 14 identified as Middle Eastern/Arab.
  • Keleg et al. (2024) analysed 15 multi-dialect datasets across 5 tasks with random annotator assignment. Inter-annotator agreement fell as sample dialectness rose. Other work found that annotators judge hate speech more harshly, and detect sarcasm less well, in dialects they do not speak.
  • Country-level benchmarks vary widely:
    • CVQA has about 300 Egyptian images.
    • Henna has 10 images for each of 11 countries.
    • ArabicMMLU draws on school exams from 8 countries.
    • AraDICE-Culture has 180 MCQs, 30 for each of 6 countries.
    • DLAMA (Arab-West) uses Wikidata triplets from 20 predicates.
  • CIDAR-MCQ-100 items have gold answers that hold only in some (mainly Gulf) countries. Examples include eating Kabsa by hand, or jellabiya and ghutra as men's clothing.
  • In a survey of over 90 papers on culture in LLMs, none explicitly operationalised what "culture" means.

Interesting ideas

  • I1. Plotting annotator agreement against a per-sample dialectness score exposes where labels become unreliable, rather than averaging the problem away.
  • I2. Assign each sample only to annotators who share its variety, since outsiders mislabel subtle phenomena like sarcasm.
  • I3. Some "gold" answers are only true for one sub-community, so a single universal key quietly encodes one group's norms.
  • I4. The same concept (who counts as "Arab") is defined differently by different Wikipedia language editions, so a concept's extension can depend on who is speaking.
  • I5. Benchmarks balanced by country (equal items per region, as in AraDICE-Culture or DLAMA) make regional bias measurable instead of hidden.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): Silver-data agreement between witness parsers could be broken down by a score of distance from standard English (dialect, register, genre), to show where silver labels should not be trusted.
  • I2 (frames): Irony and implicature judgments from LLM teachers or human checks could be tagged with the assumed speaker community, and contested cases routed to matching judges.
  • I3 (answer): Tests built from fables, tales and school courses could mark answers that rely on cultural defaults, and accept several answers or report them separately.
  • I4 (world): Ontology and knowledge-graph facts imported from Wikidata/WordNet could carry provenance and community scope, so the world of a text can hold competing definitions without merging them.
  • I5 (learn): Evaluation sets could be stratified evenly across English varieties (US, UK, Indian, African Englishes) as a control that reveals whether the parser or reader favours one variety.

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

 Institutions

🎓University of EdinburghEdinburgh · United Kingdom359 works · 77,352 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 worksSocial Sciences · 1,772 worksSafety Research · 543 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksSemitic languages · 2,908 works

Cite

Cite as Amr Keleg. 2025. LLM Alignment for the Arabs: A Homogenous Culture or Diverse Ones. Proceedings of the 3rd Workshop on Cross-Cultural Considerations in NLP (C3NLP 2025).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.c3nlp-1.1

@inproceedings{keleg2025llm,
  title = {{LLM Alignment for the Arabs: A Homogenous Culture or Diverse Ones}},
  author = {Amr Keleg},
  booktitle = {Proceedings of the 3rd Workshop on Cross-Cultural Considerations in NLP (C3NLP 2025)},
  year = {2025},
  doi = {10.18653/v1/2025.c3nlp-1.1},
  url = {https://doi.org/10.18653/v1/2025.c3nlp-1.1}
}

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