---
title: "State-of-the-Art Arabic Language Modeling with Sparse MoE"
description: "Arabic is still poorly served by large language models. 22 references. Among them: 0."
authors: ["Navan Preet Singh", "Anurag Garikipati", "Ahmed Abulkhair", "Jyani Akshay Jagdishbhai", "Atul Yaduvanshi", "Amarendra Chaudhary", "Madalina Ciobanu", "Qingqing Mao", "Ritankar Das"]
year: 2026
venue: "arXiv:2604.06421"
source: "https://arxiv.org/abs/2604.06421"
doi: "10.48550/arxiv.2604.06421"
arxiv: "2604.06421"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/singh-2026-state-art-arabic-language-modeling/"
---
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# State-of-the-Art Arabic Language Modeling with Sparse MoE Fine-Tuning and Chain-of-Thought Distillation

**Authors:** Navan Preet Singh, Anurag Garikipati, Ahmed Abulkhair, Jyani Akshay Jagdishbhai, Atul Yaduvanshi, Amarendra Chaudhary, Madalina Ciobanu, Qingqing Mao and Ritankar Das · **Year:** 2026 · **Venue:** arXiv:2604.06421 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2604.06421) · [PDF](https://arxiv.org/pdf/2604.06421) · [DOI 10.48550/arxiv.2604.06421](https://doi.org/10.48550/arxiv.2604.06421) · [arXiv 2604.06421](https://arxiv.org/abs/2604.06421) · [OpenAlex](https://openalex.org/W7152501984)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Arabic is still poorly served by large language models. Proprietary systems cannot be retrained locally, and Arabic-first models trained from scratch are expensive and still weak at reasoning. The authors adapt the open reasoning model DeepSeek-R1, which has a sparse mixture-of-experts backbone, using parameter-efficient fine-tuning instead of new pretraining. Their supervision set has 372M tokens and is filtered for benchmark contamination. About 80% of it is Arabic (Modern Standard Arabic plus Gulf, Levantine and Egyptian dialects, preferring natively written text). The remaining 20% is English, kept as an anchor against forgetting. The set is spread across several genres. Training uses a four-phase chain-of-thought distillation that adds explicit Arabic linguistic checks and regional ethical norms. The resulting Arabic-DeepSeek-R1 has the highest average on the seven-task Open Arabic LLM Leaderboard (OALL v2), ahead of the best open model and GPT-5.1. The gains are largest on grammar and safety tasks. The authors conclude that Arabic's lag comes mainly from too little specialization, not from model architecture. Adapting a strong open backbone is therefore a cheap, locally controllable route for low-resource languages.

## Key points
- OALL average of 80.18%:
  - +4.32 over D2IL-Arabic-Qwen2.5-72B
  - +2.31 over GPT-5.1
  - +5.28 over Falcon-H1-Arabic-34B-Instruct
  - +14.75 over Jais-family-30B-16k-chat
- The model is SOTA or near-SOTA on 5 of 7 benchmarks. The largest margin is on MadinahQA (+8.43 over the category leader), with strong results on AraTrust, AlGhafa and ALRAGE.
- Composition of the 372M-token supervision set:
  - 103.2M literature/critical analysis
  - 90M STEM/math/logic
  - 70M creative writing/dialogue
  - 60.2M reviews/service feedback
  - 40M legal/cultural alignment
  - 8.6M social/dialectal
- The evaluation suite also includes ArabicMMLU, Arabic EXAMS and ArbMMLU-HT. On EXAMS, Llama-3.3-70B (66.67%) still leads.
- On ALRAGE the model is only +0.16 over its baseline, though it beats the category leader by 5.84 and GPT-5.1 by 4.52. The authors explain this by their CoT data targeting reasoning rather than retrieval.

## Interesting ideas
- Keeping a fixed minority share of the original-distribution data during adaptation helps the model keep its base abilities while it specializes.
- Teacher reasoning traces are filtered through explicit language-specific checks before being used as supervision, rather than trusting the teacher's output as is.
- The training set is planned as an explicit token budget per genre, which makes the mixture auditable and easy to rebalance.
- Natively written text is preferred over machine-translated text, because translations carry artefacts of the source language.
- Results are read per benchmark, not only as an average, which showed that reasoning-focused supervision did little for retrieval tasks.

## For Mova

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

- **I1** (learn): When retraining gparse on new domains or converted UD dialects, reserve a constant slice of the original treebank in every batch so that LAS on the old data does not drift.
- **I2** (learn): Answers from the LLM teacher tiers could pass morphology and agreement checks from crate en before entering silver data, and rejected cases could feed the repair rules.
- **I3** (tree): Build silver treebanks with stated per-genre quotas (fiction, fables, school texts, dialogue) and report parser accuracy per quota.
- **I4** (tree): When collecting English text for witness-parser agreement, prefer original English sources and keep translated text apart, so translation syntax does not leak into the silver trees.
- **I5** (answer): Report school tests, maths word problems and fable QA separately to see which kind of supervision helps which reader, and target the weakest category next.

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

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## Cite

Cite as: Navan Preet Singh, Anurag Garikipati, Ahmed Abulkhair, Jyani Akshay Jagdishbhai, Atul Yaduvanshi, Amarendra Chaudhary, Madalina Ciobanu, Qingqing Mao and Ritankar Das. 2026. State-of-the-Art Arabic Language Modeling with Sparse MoE Fine-Tuning and Chain-of-Thought Distillation. arXiv:2604.06421.

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

```bibtex
@misc{singh2026state,
  title = {{State-of-the-Art Arabic Language Modeling with Sparse MoE Fine-Tuning and Chain-of-Thought Distillation}},
  author = {Navan Preet Singh, Anurag Garikipati, Ahmed Abulkhair, Jyani Akshay Jagdishbhai, Atul Yaduvanshi, Amarendra Chaudhary, Madalina Ciobanu, Qingqing Mao and Ritankar Das},
  year = {2026},
  doi = {10.48550/arxiv.2604.06421},
  eprint = {2604.06421},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2604.06421}
}
```

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## References

22 references, 11 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*