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Command R7B Arabic: a small, enterprise-focused, multilingual, and culturally aware Arabic LLM

Paper · 2025 · Yazeed Alnumay, Alexandre Barbet, Anna Bialas et al.

Summary

Enterprise-grade Arabic LLMs are hard to build because little digitized Arabic text exists, and English data often fails to carry over because of differences in morphology, syntax and culture. The Cohere team adapts an existing open-weight multilingual checkpoint, Command R7B, rather than pretraining from scratch. They focus on post-training in three stages: supervised fine-tuning, offline preference tuning and iterative preference tuning, with the last two using DPO.

Data comes from public Arabic sets such as ArMATH and ArabicaQA, plus synthetic data. The synthetic data is seeded by expert-translated IFEval instructions and filtered through "Multilingual Arbitrage". Each candidate dataset is admitted only if it measurably improves a fixed base mixture. Specialised experts trained at each stage are merged by linear weight averaging.

The resulting 7B model, Command R7B Arabic, leads its size class on most Arabic benchmarks while keeping its general English abilities. This shows that careful post-training alone can substantially localise a small model for a lower-resource language.

Key points

  • Arabic benchmark average:
    • Command R7B Arabic scores 69.3, against 65.8 for the base Command R7B and 67.0 for Gemma 9B.
    • It is best on AlGhafa-Native (82.2), IFEval AR (69.0), TyDiQA-GoldP Arabic (83.0) and FaithEval Arabic (51.6).
    • It ranks third on ArabicMMLU (60.9 vs Gemma's 62.4).
  • IFEval AR rises from 57.8 (base) to 69.0. The translated instruction set gained two Arabic-only constraints: adding a given number of diacritics, and opening sentences with a specified verb.
  • General ability is largely retained. The Open LLM Leaderboard average is 31.4 vs 31.6 for the base model, with top scores in its class on IfEval and MuSR. MATH dropped, which may reflect a benchmark change.
  • On Arabic mArenaHard, judged by GPT-4o, the model wins pairwise comparisons against Gemma 2 9B, Llama 3.1 8B, Qwen 2.5 7B, Ministral 8B, Jais 30B and its own base.
  • Expert merging used only linear interpolation of weights. Several weighting schemes were tried, and equal weights for all experts worked best.
  • Stated limitations:
    • The work covers only Modern Standard Arabic, not dialects.
    • The benchmarks are proxies for real enterprise deployment.

Interesting ideas

  • I1. Ablation-gated data admission. A fixed base mixture is trained with one candidate dataset at a time, and the candidate is kept only if a benchmark harness shows gains. Otherwise it is repaired and re-tested, so every dataset in the final mix earns its place.
  • I2. Train experts, then average. Instead of retraining one generalist on all data, domain experts are trained separately and averaged with equal weights, which saves compute. The cost is harder replication.
  • I3. Localise constraints instead of translating them. Constraints like word counts or comma bans become odd or meaningless when translated, so they were replaced or supplemented with language-native ones (diacritics, verb-initial sentences).
  • I4. Layered filtering of synthetic data. Synthetic outputs pass a reward model and a panel of LLM judges for language quality. Preference pairs are chosen by the largest reward gap, which gives clear contrasts.
  • I5. Faithfulness under misleading context. FaithEval-style evaluation gives the model contexts that are unanswerable, inconsistent or counterfactual. It checks whether answers stay grounded in the provided text.

For Mova

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

  • I1 (learn): Each new batch of silver trees agreed by witness parsers could be gated the same way, admitted only if held-out LAS on a fixed treebank mix improves. A shuffled-label batch could serve as the negative control.
  • I2 (tree): gparse could train genre experts (fables, maths word problems, expository text) from one checkpoint and average them. This would avoid a full retrain whenever a new genre treebank appears.
  • I3 (answer): Test items borrowed from other-language resources should be rewritten around genuinely English phenomena, such as particle verbs or do-support, rather than mechanically ported.
  • I4 (learn): LLM teacher tiers could score candidate frame or parse repairs this way. Only maximally separated good/bad pairs would feed the self-induced repair rules, reducing noisy supervision.
  • I5 (world): Counterfactual fables, for example where a stone floats, could verify that the world-of-the-text overrides compiled WordNet/Wikidata knowledge. Unanswerable questions should yield an explicit "not stated".

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, topics and concepts

📚 Shelves 4The 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 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3,320 worksSemitic languages · 2,908 worksMultilingual & cross-lingual NLP · 828 works

Cite

Cite as 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. Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.africanlp-1.17

@inproceedings{alnumay2025command,
  title = {{Command R7B Arabic: a small, enterprise-focused, multilingual, and culturally aware Arabic LLM}},
  author = {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},
  booktitle = {Proceedings of the Sixth Workshop on African Natural Language Processing (AfricaNLP 2025)},
  year = {2025},
  doi = {10.18653/v1/2025.africanlp-1.17},
  url = {https://doi.org/10.18653/v1/2025.africanlp-1.17}
}

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