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

Amr Keleg

TL;DR

The paper challenges the assumption that there is a single Arabic culture governing LLM alignment and highlights substantial intraregional diversity. It surveys current Arabic-focused datasets and alignment practices, exposing biases arising from homogenization and dialect-level misalignments. It proposes four concrete steps—diverse research teams, topic-interest mapping, language-variety identification, and more inclusive alignment data—to build culturally representative systems for Arabic speakers. By foregrounding cultural nuance, the work aims to reduce Western-centric bias and improve practical AI assistance across the Arab world.

Abstract

Large language models (LLMs) have the potential of being useful tools that can automate tasks and assist humans. However, these models are more fluent in English and more aligned with Western cultures, norms, and values. Arabic-specific LLMs are being developed to better capture the nuances of the Arabic language, as well as the views of the Arabs. Yet, Arabs are sometimes assumed to share the same culture. In this position paper, I discuss the limitations of this assumption and provide preliminary thoughts for how to build systems that can better represent the cultural diversity within the Arab world. The invalidity of the cultural homogeneity assumption might seem obvious, yet, it is widely adopted in developing multilingual and Arabic-specific LLMs. I hope that this paper will encourage the NLP community to be considerate of the cultural diversity within various communities speaking the same language.

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

TL;DR

The paper challenges the assumption that there is a single Arabic culture governing LLM alignment and highlights substantial intraregional diversity. It surveys current Arabic-focused datasets and alignment practices, exposing biases arising from homogenization and dialect-level misalignments. It proposes four concrete steps—diverse research teams, topic-interest mapping, language-variety identification, and more inclusive alignment data—to build culturally representative systems for Arabic speakers. By foregrounding cultural nuance, the work aims to reduce Western-centric bias and improve practical AI assistance across the Arab world.

Abstract

Large language models (LLMs) have the potential of being useful tools that can automate tasks and assist humans. However, these models are more fluent in English and more aligned with Western cultures, norms, and values. Arabic-specific LLMs are being developed to better capture the nuances of the Arabic language, as well as the views of the Arabs. Yet, Arabs are sometimes assumed to share the same culture. In this position paper, I discuss the limitations of this assumption and provide preliminary thoughts for how to build systems that can better represent the cultural diversity within the Arab world. The invalidity of the cultural homogeneity assumption might seem obvious, yet, it is widely adopted in developing multilingual and Arabic-specific LLMs. I hope that this paper will encourage the NLP community to be considerate of the cultural diversity within various communities speaking the same language.

Paper Structure

This paper contains 14 sections, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Arabs - a Single or Multiple Cultures?
  3. View #1 - One Culture
  4. View #2 - Multiple Unrelated Cultures
  5. How is the Arabic Culture Currently Represented?
  6. Classical Task-specific Datasets
  7. Less Subjective Culture Understanding Benchmarks
  8. Values Alignment Datasets
  9. Recommendations
  10. Step #1 - Improving the Diversity of the Research Teams
  11. Step #2 - Understanding the Topics of Interest of the Speakers across the Arab World
  12. Step #3 - Identifying the Languages/Varieties that Arabic Speakers Use on Engaging with Technologies
  13. Step #4 - Collecting More Inclusive Alignment Data
  14. Conclusion

Figures (2)

  • Figure 1: A visualization of the areas with substantial cultural similarities as in bird-2024-must. Arabic speakers (green color) are grouped into a single region.
  • Figure 2: Two statements from the ACVA benchmark showcasing misrepresentation of the cultural nuances within the Arab world. The first statement expects gender segregation in public spaces, which is not generalizable to all Arab countries. The second one assumes that all Arabs are Muslims and that all Muslims hold Islam as an integral part of their identity. Adding a quantifier like "معظم العرب(Most Arabs)" would make the statement more precise and less controversial.