---
title: "Cultural Benchmarking of LLMs in Standard and Dialectal Arabic"
description: "The authors build ArabCulture-Dialogue by turning each item of the existing ArabCulture benchmark into… 40 references, cited by 1 paper here."
authors: ["Muhammad Dehan Al Kautsar", "Saeed Almheiri", "Momina Ahsan", "Bilal Elbouardi", "Younes Samih", "Sarfraz Ahmad", "Amr Keleg", "Omar El Herraoui", "Kareem Elzeky", "Abed Alhakim Freihat", "Mohamed Anwar", "Zhuohan Xie", "Junhong Liang", "Mohammad Rustom Al Nasar", "Preslav Nakov", "Fajri Koto"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.963/"
doi: "10.18653/v1/2026.acl-long.963"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/kautsar-2026-cultural-benchmarking-llms-standard-dialectal/"
---
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# Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues

**Authors:** Muhammad Dehan Al Kautsar, Saeed Almheiri, [Momina Ahsan](https://md.research.elamur.ai/free/en/authors/momina-ahsan/), [Bilal Elbouardi](https://md.research.elamur.ai/free/en/authors/bilal-elbouardi/), [Younes Samih](https://md.research.elamur.ai/free/en/authors/younes-samih/), [Sarfraz Ahmad](https://md.research.elamur.ai/free/en/authors/sarfraz-ahmad/), [Amr Keleg](https://md.research.elamur.ai/free/en/authors/amr-keleg/), [Omar El Herraoui](https://md.research.elamur.ai/free/en/authors/omar-el-herraoui/), Kareem Elzeky, [Abed Alhakim Freihat](https://md.research.elamur.ai/free/en/authors/abed-alhakim-freihat/), [Mohamed Anwar](https://md.research.elamur.ai/free/en/authors/mohamed-anwar/), [Zhuohan Xie](https://md.research.elamur.ai/free/en/authors/zhuohan-xie/), Junhong Liang, Mohammad Rustom Al Nasar, [Preslav Nakov](https://md.research.elamur.ai/free/en/authors/preslav-nakov/) and [Fajri Koto](https://md.research.elamur.ai/free/en/authors/fajri-koto/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.963/) · [PDF](https://aclanthology.org/2026.acl-long.963.pdf) · [DOI 10.18653/v1/2026.acl-long.963](https://doi.org/10.18653/v1/2026.acl-long.963) · [OpenAlex](https://openalex.org/W7166798407)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [1](https://md.research.elamur.ai/free/en/papers/kautsar-2026-cultural-benchmarking-llms-standard-dialectal/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Arabic cultural benchmarks usually test isolated, single-turn questions in Modern Standard Arabic (MSA). Everyday cultural knowledge, however, is exchanged in conversation and in regional dialects. The authors build ArabCulture-Dialogue by turning each item of the existing ArabCulture benchmark into a short multi-turn dialogue. Every dialogue ends with three candidate replies, and only one of them is culturally appropriate. An LLM wrote the initial drafts. Native annotators from each country then rewrote them, localized them into the national dialect without using LLMs, and cross-checked one another's work. The result is a parallel MSA–dialect resource covering 13 countries.

The dataset supports three tasks: picking the culturally fitting reply, translating between MSA and the dialects, and generating text in a requested dialect. Arabic-centric, multilingual and proprietary models all scored lower in the dialectal setting than in MSA on every task. Smaller open-weight models were the weakest. This suggests that MSA-only evaluation probably overstates how well models handle real, dialect-mediated cultural interaction.

## Key points
- ArabCulture-Dialogue covers 13 Arab countries, 12 daily-life topics and 54 subtopics, with 343,804 words in total. It is released on Hugging Face (Almheiri/ArabCulture-Dialogue).
- The source is ArabCulture (Sadallah et al., 2025), which has 3,482 MCQ items. The authors kept its country distribution and subtopic coverage.
- Curation involved 26 native speakers, two per country. A separate annotator did an independent quality check of each dialect translation.
- Annotators removed two shortcut patterns:
  - answer leakage inside the dialogue;
  - tone or structure differences that made the correct option stand out.
- Dialect steering was judged in two ways: an LLM judge scored quality, and GlotLID checked strict ISO-code dialect accuracy.
  - SFT of multilingual models (LLaMA-3.1-8B-it, Qwen3-8B, Gemma-2-9B-it) raised judge scores but reduced their ability to answer in MSA.
  - SFT improved dialect identity unevenly across countries, with Moroccan gaining most.
  - Some open 7B models were close to random guessing on the MCQ task.

## Interesting ideas
- Distractors were edited so the right answer cannot be spotted from tone, length or leaked hints, which forces real reasoning instead of pattern matching; this generalizes to any multiple-choice evaluation.
- Each item exists in both a standard and a colloquial version with identical content, so any score drop can be attributed purely to the register rather than to the task.
- Generation is scored on two separate axes: an independent language-ID tool checks whether the output is in the requested variety, and a judge rates quality.
- The fine-tuning results showed a trade-off: training toward dialect output improved quality but weakened the model's standard-variety output, a regression an aggregate score could hide.
- Existing single-turn items were expanded into multi-turn dialogues while their original topic distribution was preserved, reusing a validated resource for a harder setting.

## For Mova

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

- **I1** (answer): Audit school-test and fable QA items for surface cues that separate correct from wrong options, and add cue-neutralized variants as negative controls.
- **I2** (tree): Build small parallel sets of formal vs. informal/dialectal English sentences and measure parser LAS on both to quantify the register penalty after UD dialect conversion.
- **I3** (back): Pair generation quality checks with an independent register or variety classifier, so that fluent but off-target outputs are caught.
- **I4** (learn): Whenever training on silver data from a new domain, re-run the original-domain benchmarks to detect this kind of silent erosion.
- **I5** (answer): Convert existing fable QA items into short dialogues, so readers must track coreference and speaker intent across turns before answering.

*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

- [IBM](https://md.research.elamur.ai/free/en/companies/ibm/) — Company, Armonk, United States · 139 works · 70,691 citations
- [Mohamed bin Zayed University of Artificial Intelligence](https://md.research.elamur.ai/free/en/universities/mohamed-bin-zayed-university-of-artificial-intelligence/) — University, Abu Dhabi, United Arab Emirates · 124 works · 5,676 citations

Also on the byline: [American University in the Emirates](https://ror.org/056c6h780).

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works

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## Cite

Cite as: Muhammad Dehan Al Kautsar, Saeed Almheiri, Momina Ahsan, Bilal Elbouardi, Younes Samih, Sarfraz Ahmad, Amr Keleg, Omar El Herraoui, Kareem Elzeky, Abed Alhakim Freihat, Mohamed Anwar, Zhuohan Xie, Junhong Liang, Mohammad Rustom Al Nasar, Preslav Nakov and Fajri Koto. 2026. Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{kautsar2026cultural,
  title = {{Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues}},
  author = {Muhammad Dehan {Al Kautsar}, Saeed Almheiri, Momina Ahsan, Bilal Elbouardi, Younes Samih, Sarfraz Ahmad, Amr Keleg, Omar {El Herraoui}, Kareem Elzeky, Abed Alhakim Freihat, Mohamed Anwar, Zhuohan Xie, Junhong Liang, Mohammad Rustom {Al Nasar}, Preslav Nakov and Fajri Koto},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.963},
  url = {https://doi.org/10.18653/v1/2026.acl-long.963}
}
```

- [← previous CultRAG at SemEval-2026 Task 7: Hybrid Sparse-Dense Retrieval with Entity-Centric Knowledge Bases for Cultural MCQ Answering](https://md.research.elamur.ai/free/en/papers/singh-2026-cultrag-semeval-2026-task-7/)
- [next → Culturally-Aware Image Captioning for Guaraní with Multimodal Prompting: IUHoosiers at AmericasNLP 2026](https://md.research.elamur.ai/free/en/papers/shi-2026-culturally-aware-image-captioning-guarani/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/11/)

## References

40 references, 29 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Ummar Abbas, Mohammad Shahmeer Ahmad, Firoj Alam, Enes Altinisik, Ehsannedin Asgari, Yazan https://dubairdi.ae/Boshmaf, Sabri Boughorbel, Sanjay Chawla, Shammur Chowdhury, Fahim Dalvi, Kareem Darwish, Nadir Durrani, Mohamed Elfeky, Ahmed Elmagarmid, Mohamed Eltabakh, Masoomali Fatehkia, Anastasios Fragkopoulos, Maram Hasanain, Majd Hawasly, and 22 others. 2025. [Fanar: An Arabic-centric multimodal generative AI platform](https://md.research.elamur.ai/free/en/papers/abbas-2025-fanar-arabic-centric-multimodal-generative/). ArXiv preprint, arXiv:2501.13944. [↗](https://arxiv.org/abs/2501.13944)
2. Ahmed Abdelali, Hamdy Mubarak, Younes Samih, Sabit Hassan, and Kareem Darwish. 2021. [QADI: Arabic dialect identification in the wild](https://md.research.elamur.ai/free/en/papers/abdelali-2021-qadi-arabic-dialect-identification-wild/). In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 1–10, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
3. Muhammad Abdul-Mageed, Amr Keleg, AbdelRahim Elmadany, Chiyu Zhang, Injy Hamed, Walid Magdy, Houda Bouamor, and Nizar Habash. 2024. [NADI 2024: The fifth nuanced Arabic dialect identification shared task](https://md.research.elamur.ai/free/en/papers/mageed-2024-nadi-2024-fifth-nuanced-arabic/). In Proceedings of the Second Arabic Natural Language Processing Conference, pages 709–728, Bangkok, Thailand. Association for Computational Linguistics.
4. Muhammad Abdul-Mageed, Chiyu Zhang, AbdelRahim Elmadany, Houda Bouamor, and Nizar Habash. 2021. [NADI 2021: The second nuanced Arabic dialect identification shared task](https://md.research.elamur.ai/free/en/papers/mageed-2021-nadi-2021-second-nuanced-arabic/). In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 244–259, Kyiv, Ukraine (Virtual). Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2103.08466)
5. Yazeed Alnumay, Alexandre Barbet, Anna Bialas, William Darling, Shaan Desai, Joan Devassy, Kyle Duffy, Stephanie Howe, Olivia Lasche, Justin Lee, Anirudh Shrinivason, and Jennifer Tracey. 2025. [Command R7B Arabic: a small, enterprise-focused, multilingual, and culturally aware Arabic LLM](https://md.research.elamur.ai/free/en/papers/alnumay-2025-command-r7b-arabic-small-enterprise/). In Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025), pages 126–135, Vienna, Austria. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2503.14603)
6. Fakhraddin Alwajih, Abdellah El Mekki, Samar Mohamed Magdy, AbdelRahim A. Elmadany, Omer Nacar, El Moatez Billah Nagoudi, Reem Abdel-Salam, Hanin Atwany, Youssef Nafea, Abdulfattah Mohammed Yahya, Rahaf Alhamouri, Hamzah A. Alsayadi, Hiba Zayed, Sara Shatnawi, Serry Sibaee, Yasir Ech-chammakhy, Walid Al-Dhabyani, Marwa Mohamed Ali, Imen Jarraya, and 25 others. 2025a. [Palm: A culturally inclusive and linguistically diverse dataset for Arabic LLMs](https://md.research.elamur.ai/free/en/papers/alwajih-2025-palm-culturally-inclusive-linguistically-diverse/). In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 32871–32894, Vienna, Austria. Association for Computational Linguistics.
7. Fakhraddin Alwajih, Abdellah El Mekki, Hamdy Mubarak, Majd Hawasly, Abubakr Mohamed, and Muhammad Abdul-Mageed. 2025b. [PalmX 2025: The first shared task on benchmarking LLMs on Arabic and islamic culture](https://md.research.elamur.ai/free/en/papers/alwajih-2025-palmx-2025-first-shared-task/). In Proceedings of The Third Arabic Natural Language Processing Conference: Shared Tasks, pages 774–789, Suzhou, China. Association for Computational Linguistics. in NLP (C3NLP), pages 53–67, Dubrovnik, Croatia. Association for Computational Linguistics.
8. Fakhraddin Alwajih, El Moatez Billah Nagoudi, Gagan Bhatia, Abdelrahman Mohamed, and Muhammad Abdul-Mageed. 2024. [Peacock: A family of Arabic multimodal large language models and benchmarks](https://md.research.elamur.ai/free/en/papers/alwajih-2024-peacock-family-arabic-multimodal-large/). In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12753–12776, Bangkok, Thailand. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2403.01031)
9. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. [BERT: Pre-training of deep bidirectional transformers for language understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/). In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics. [↗](https://doi.org/10.18653/v1/n19-1423)
10. Lama Ayash, Hassan Alhuzali, Ashwag Alasmari, and Sultan Aloufi. 2025. SaudiCulture: A benchmark for evaluating large language models’ cultural competence within Saudi Arabia. Journal of King Saud University Computer and Information Sciences, 37(6):123. [↗](https://doi.org/10.1007/s44443-025-00137-9)
11. M Saiful Bari, Yazeed Alnumay, Norah Alzahrani, Nouf Alotaibi, Hisham Alyahya, AlRashed AlRashed, Faisal Mirza, Shaykhah Alsubaie, Hassan Alahmed, Ghadah Alabduljabbar, Raghad Alkhathran, Yousef Almushayqih, Raneem Alnajim, Salman I Alsubaihi, Maryam Al Mansour, Saad Hassan, Majed Alrubaian, Ali Alammari, Zaki Alawami, and 7 others. 2025. [Allam: Large language models for arabic and english](https://md.research.elamur.ai/free/en/papers/bari-2024-allam-large-language-models-arabic/). In International Conference on Learning Representations, volume 2025, pages 34179–34214, Singapore.
12. Hunzalah Hassan Bhatti and Firoj Alam. 2025. [Beyond MCQ: An open-ended Arabic cultural QA benchmark with dialect variants](https://md.research.elamur.ai/free/en/papers/bhatti-2026-beyond-mcq-open-ended-arabic/). Preprint, arXiv:2510.24328. [↗](https://arxiv.org/abs/2510.24328)
13. Su Lin Blodgett, Lisa Green, and Brendan O’Connor. 2016. [Demographic dialectal variation in social media: A case study of African-American English](https://md.research.elamur.ai/free/en/papers/blodgett-2016-demographic-dialectal-variation-social-media/). In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1119–1130, Austin, Texas. Association for Computational Linguistics. [↗](https://doi.org/10.18653/v1/d16-1120)
14. Houda Bouamor, Nizar Habash, and Kemal Oflazer. 2014. [A multidialectal parallel corpus of Arabic](https://md.research.elamur.ai/free/en/papers/bouamor-2014-multidialectal-parallel-corpus-arabic/). In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pages 1240–1245, Reykjavik, Iceland. European Language Resources Association (ELRA).
15. Houda Bouamor, Nizar Habash, Mohammad Salameh, Wajdi Zaghouani, Owen Rambow, Dana Abdulrahim, Ossama Obeid, Salam Khalifa, Fadhl Eryani, Alexander Erdmann, and Kemal Oflazer. 2018. The MADAR Arabic dialect corpus and lexicon. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA). [find](https://scholar.google.com/scholar?q=Houda+Bouamor%2C+Nizar+Habash%2C+Mohammad+Salameh%2C+Wajdi+Zaghouani%2C+Owen+Rambow%2C+Dana+Abdulrahim%2C+Ossama+Obeid%2C+Salam+Khalifa%2C+Fadhl+Eryani%2C+Alexander+Erdmann%2C+and+Kemal+Oflazer%2E+2018%2E+The+MADAR+Arabic+di)
16. Yong Cao, Li Zhou, Seolhwa Lee, Laura Cabello, Min Chen, and Daniel Hershcovich. 2023. [Assessing cross-cultural alignment between ChatGPT and human societies: An empirical study](https://md.research.elamur.ai/free/en/papers/cao-2023-assessing-cross-cultural-alignment-between/). In Proceedings of the First Workshop on Cross-Cultural Considerations Abdellah El Mekki, Houdaifa Atou, Omer Nacar, Shady Shehata, and Muhammad Abdul-Mageed. 2025. NileChat: Towards linguistically diverse and culturally aware LLMs for local communities. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 10978–11002, Suzhou, China. Association for Computational Linguistics.
17. Google DeepMind. 2024. Gemma: Open models based on Gemini research and technology. ArXiv preprint, arXiv:2403.08295. [↗](https://arxiv.org/abs/2403.08295)
18. Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, and 542 others. 2024. [The Llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). Preprint, arXiv:2407.21783. [↗](https://arxiv.org/abs/2407.21783)
19. Nizar Y. Habash. 2010. Introduction to Arabic Natural Language Processing. Morgan & Claypool Publishers. [↗](https://doi.org/10.1007/978-3-031-02139-8)
20. Hasan Abed Al Kader Hammoud, Mohamad Bilal Zbib, and Bernard Ghanem. 2026. [Hala technical report building Arabic-centric instruction & translation models at scale](https://md.research.elamur.ai/free/en/papers/hammoud-2026-hala-technical-report-building-arabic/). In Proceedings of the 2nd Workshop on NLP for Languages Using Arabic Script, pages 236–244, Rabat, Morocco. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2509.14008)
21. Daniel Hershcovich, Stella Frank, Heather Lent, Miryam de Lhoneux, Mostafa Abdou, Stephanie Brandl, Emanuele Bugliarello, Laura Cabello Piqueras, Ilias Chalkidis, Ruixiang Cui, Constanza Fierro, Katerina Margatina, Phillip Rust, and Anders Søgaard. 2022. [Challenges and strategies in cross-cultural NLP](https://md.research.elamur.ai/free/en/papers/hershcovich-2022-challenges-strategies-cross-cultural-nlp/). In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6997–7013, Dublin, Ireland. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2203.10020)
22. Valentin Hofmann, Pratyusha Ria Kalluri, Dan Jurafsky, and Sharese King. 2024. [Dialect prejudice predicts AI decisions about people’s character, employability, and criminality](https://md.research.elamur.ai/free/en/papers/hofmann-2024-dialect-prejudice-predicts-ai-decisions/). Preprint, arXiv:2403.00742. [↗](https://arxiv.org/abs/2403.00742)
23. Clive Holes. 2006. The Arabic dialects of Arabia. In Proceedings of the Seminar for Arabian Studies, volume 36, pages 25–34, London, United Kingdom. Ar-chaeopress. [find](https://scholar.google.com/scholar?q=Clive+Holes%2E+2006%2E+The+Arabic+dialects+of+Arabia%2E+In+Proceedings+of+the+Seminar+for+Arabian+Studies%2C+volume+36%2C+pages+25%E2%80%9334%2C+London%2C+United+Kingdom%2E+Ar%2Dchaeopress%2E)
24. Dirk Hovy. 2015. [Demographic factors improve classification performance](https://md.research.elamur.ai/free/en/papers/hovy-2015-demographic-factors-improve-classification-performance/). In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 752–762, Beijing, China. Association for Computational Linguistics.
25. Inception. 2024. Jais family model card. [find](https://scholar.google.com/scholar?q=Inception%2E+2024%2E+Jais+family+model+card%2E)
26. David Jurgens, Yulia Tsvetkov, and Dan Jurafsky. 2017. Incorporating dialectal variability for socially equitable language identification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 51–57, Vancouver, Canada. Association for Computational Linguistics. [↗](https://doi.org/10.18653/v1/p17-2009)
27. Karima Kadaoui, Hanin Atwany, Hamdan Al-Ali, Abdelrahman Mohamed, Ali Mekky, Sergei Tilga, Natalia Fedorova, Ekaterina Artemova, Hanan Aldarmaki, and Yova Kementchedjhieva. 2026. [JEEM: Vision-language understanding in four Arabic dialects](https://md.research.elamur.ai/free/en/papers/kadaoui-2026-jeem-vision-language-understanding-four/). In Findings of the Association for Computational Linguistics: EACL 2026, pages 331–354, Rabat, Morocco. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2503.21910)
28. Amir Hossein Kargaran, Ayyoob Imani, François Yvon, and Hinrich Schuetze. 2023. [GlotLID: Language identification for low-resource languages](https://md.research.elamur.ai/free/en/papers/kargaran-2023-glotlid-language-identification-low-resource/). In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 6155–6218, Singapore. Association for Computational Linguistics.
29. Amr Keleg, Sharon Goldwater, and Walid Magdy. 2023. [ALDi: Quantifying the Arabic level of dialectness of text](https://md.research.elamur.ai/free/en/papers/keleg-2023-aldi-quantifying-arabic-level-dialectness/). In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 10597–10611, Singapore. Association for Computational Linguistics.
30. Amr Keleg and Walid Magdy. 2023. [Arabic dialect identification under scrutiny: Limitations of single-label classification](https://md.research.elamur.ai/free/en/papers/keleg-2023-arabic-dialect-identification-under-scrutiny/). In Proceedings of ArabicNLP 2023, pages 385–398, Singapore (Hybrid). Association for Computational Linguistics.
31. Fajri Koto, Rahmad Mahendra, Nurul Aisyah, and Timothy Baldwin. 2024. [IndoCulture: Exploring geographically influenced cultural commonsense reasoning across eleven Indonesian provinces](https://md.research.elamur.ai/free/en/papers/koto-2024-indoculture-exploring-geographically-influenced-cultural/). Transactions of the Association for Computational Linguistics, 12:1703–1719. [↗](https://doi.org/10.1162/tacl_a_00726)
32. Kathrein Abu Kwaik, Motaz Saad, Stergios Chatzikyriakidis, and Simon Dobnik. 2018. A lexical distance study of Arabic dialects. Procedia Computer Science, 142:2–13. Arabic Computational Linguistics. [find](https://scholar.google.com/scholar?q=Kathrein+Abu+Kwaik%2C+Motaz+Saad%2C+Stergios+Chatzikyriakidis%2C+and+Simon+Dobnik%2E+2018%2E+A+lexical+distance+study+of+Arabic+dialects%2E+Procedia+Computer+Science%2C+142%3A2%E2%80%9313%2E+Arabic+Computational+Linguistics%2E)
33. Samar Mohamed Magdy, Sang Yun Kwon, Fakhraddin Alwajih, Safaa Taher Abdelfadil, Shady Shehata, and Muhammad Abdul-Mageed. 2025. [JAWAHER: A multidialectal dataset of Arabic proverbs for LLM benchmarking](https://md.research.elamur.ai/free/en/papers/magdy-2025-jawaher-multidialectal-dataset-arabic-proverbs/). In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 12320–12341, Albuquerque, New Mexico. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2503.00231)
34. Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002. [Bleu: a method for automatic evaluation of machine translation](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/). In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311–318, Philadelphia, Pennsylvania, USA. Association for Computational Linguistics. [↗](https://doi.org/10.3115/1073083.1073135)
35. Abdelrahman Sadallah, Junior Cedric Tonga, Khalid Almubarak, Saeed Almheiri, Farah Atif, Chatrine Qwaider, Karima Kadaoui, Sara Shatnawi, Yaser Alesh, and Fajri Koto. 2025. Commonsense reasoning in Arab culture. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7695– 7710, Vienna, Austria. Association for Computational Linguistics. [↗](https://doi.org/10.48550/arxiv.2502.12788)
36. Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, Satheesh Katipomu, Haonan Li, Fajri Koto, William Marshall, Gurpreet Gosal, Cynthia Liu, Zhiming Chen, Osama Mohammed Afzal, Samta Kamboj, Onkar Pandit, Rahul Pal, Lalit Pradhan, Zain Muhammad Mujahid, Massa Baali, Xudong Han, Sondos Mahmoud Bsharat, and 13 others. 2023. [Jais and Jais-chat: Arabic-centric foundation and instruction-tuned open generative large language models](https://md.research.elamur.ai/free/en/papers/sengupta-2023-jais-jais-chat-arabic-centric/). Preprint, arXiv:2308.16149. [↗](https://arxiv.org/abs/2308.16149)
37. SILMA-AI. 2024. SILMA 9B Instruct v1.0. [https://huggingface.co/silma-ai/](https://huggingface.co/silma-ai/) SILMA-9B-Instruct-v1.0. [↗](https://huggingface.co/silma-ai/)
38. Qwen Team. 2025. [Qwen3 technical report](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/). Preprint, arXiv:2505.09388. [↗](https://arxiv.org/abs/2505.09388)
39. UNESCO. 2025. World Arabic language day. [https://www.unesco.org/en/](https://www.unesco.org/en/) world-arabic-language-day. Published: December 18, 2025; Accessed: 2026. [↗](https://www.unesco.org/en/)
40. Omar F. Zaidan and Chris Callison-Burch. 2014. [Arabic dialect identification](https://md.research.elamur.ai/free/en/papers/zaidan-2014-arabic-dialect-identification/). Computational Linguistics, 40(1):171–202. [↗](https://doi.org/10.1162/coli_a_00169)

*Updated 1 November 2026*