# Command R7B Arabic: A Small, Enterprise Focused, Multilingual, and Culturally Aware Arabic LLM

arXiv: https://arxiv.org/abs/2503.14603 · HTML: https://mlsanity.com/paper/2503.14603/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 51 references that are in the map.
- First posted on arXiv 18 Mar 2025.

## Builds on (most cited first, 30 of 31)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [From Crowdsourced Data to High-Quality Benchmarks: Arena-Hard and BenchBuilder Pipeline](https://mlsanity.com/paper/2406.11939/) (2024)
- [TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages](https://mlsanity.com/paper/2003.05002/) (2020)
- [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://mlsanity.com/paper/2308.16884/) (2023)
- [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://mlsanity.com/paper/2402.07827/) (2024)
- [Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities](https://mlsanity.com/paper/2408.07666/) (2024)
- [XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation](https://mlsanity.com/paper/2004.01401/) (2020)
- [MuSR: Testing the Limits of Chain-of-thought with Multistep Soft Reasoning](https://mlsanity.com/paper/2310.16049/) (2023)
- [Arcee's MergeKit: A Toolkit for Merging Large Language Models](https://mlsanity.com/paper/2403.13257/) (2024)
- [Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier](https://mlsanity.com/paper/2412.04261/) (2024)
- [ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic](https://mlsanity.com/paper/2402.12840/) (2024)
- [FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"](https://mlsanity.com/paper/2410.03727/) (2024)
- [AceGPT, Localizing Large Language Models in Arabic](https://mlsanity.com/paper/2309.12053/) (2023)
- [Fanar: An Arabic-Centric Multimodal Generative AI Platform](https://mlsanity.com/paper/2501.13944/) (2025)
- [EXAMS: A Multi-Subject High School Examinations Dataset for Cross-Lingual and Multilingual Question Answering](https://mlsanity.com/paper/2011.03080/) (2020)
- [ALLaM: Large Language Models for Arabic and English](https://mlsanity.com/paper/2407.15390/) (2024)
- [Neural Arabic Question Answering](https://mlsanity.com/paper/1906.05394/) (2019)
- [Lost in Translation: Large Language Models in Non-English Content Analysis](https://mlsanity.com/paper/2306.07377/) (2023)
- [ArabicaQA: A Comprehensive Dataset for Arabic Question Answering](https://mlsanity.com/paper/2403.17848/) (2024)
- [AL-QASIDA: Analyzing LLM Quality and Accuracy Systematically in Dialectal Arabic](https://mlsanity.com/paper/2412.04193/) (2024)
- [Alignment at Pre-training! Towards Native Alignment for Arabic LLMs](https://mlsanity.com/paper/2412.03253/) (2024)

## Built on by (most cited first, 16 of 16)

- [DialectalArabicMMLU: Benchmarking Dialectal Capabilities in Arabic and Multilingual Language Models](https://mlsanity.com/paper/2510.27543/) (2025)
- [FanarGuard: A Culturally-Aware Moderation Filter for Arabic Language Models](https://mlsanity.com/paper/2511.18852/) (2025)
- [LAILA: A Large Trait-Based Dataset for Arabic Automated Essay Scoring](https://mlsanity.com/paper/2512.24235/) (2025)
- [MedAraBench: Large-Scale Arabic Medical Question Answering Dataset and Benchmark](https://mlsanity.com/paper/2602.01714/) (2026)
- [Structuring the Space of Sociotechnical Alignment](https://mlsanity.com/paper/2607.01250/) (2026)
- [Alexandria: A Multi-Domain Dialectal Arabic Machine Translation Dataset for Culturally Inclusive and Linguistically Diverse LLMs](https://mlsanity.com/paper/2601.13099/) (2026)
- [Buy versus Build an LLM: A Decision Framework for Governments](https://mlsanity.com/paper/2602.13033/) (2026)
- [Think in English, Answer in Korean: Efficient Adaptation of Multilingual Tool-Using Agents](https://mlsanity.com/paper/2606.31648/) (2026)
- [OptiMer: Optimal Distribution Vector Merging Is Better than Data Mixing for Continual Pre-Training](https://mlsanity.com/paper/2603.28858/) (2026)
- [Context-Aware Dialectal Arabic Machine Translation with Interactive Region and Register Selection](https://mlsanity.com/paper/2604.06456/) (2026)
- [Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues](https://mlsanity.com/paper/2605.00119/) (2026)
- [Redteaming Leading Arabic LLMs with ASAS](https://mlsanity.com/paper/2608.21985/) (2026)
- [E-CONAN (Entailment, CONtradition And Neutral) Benchmarks: Arabic Textual Entailment and Natural Inference Datasets](https://mlsanity.com/paper/2609.11334/) (2026)
- [Evaluating Communicative Success in Machine-Translated Conversation](https://mlsanity.com/paper/2609.19885/) (2026)
- [E-CONAN (Entailment, CONtradition And Neutral) Diagnostics Dataset Investigating Linguistic Phenomena in Arabic Natural Language Understanding](https://mlsanity.com/paper/2609.33530/) (2026)
- [AraDynFact: Dynamic Evaluation of Factual Knowledge in Arabic](https://mlsanity.com/paper/2609.35461/) (2026)

## Abstract (arXiv)

Building high-quality large language models (LLMs) for enterprise Arabic applications remains challenging due to the limited availability of digitized Arabic data. In this work, we present a data synthesis and refinement strategy to help address this problem, namely, by leveraging synthetic data generation and human-in-the-loop annotation to expand our Arabic training corpus. We further present our iterative post training recipe that is essential to achieving state-of-the-art performance in aligning the model with human preferences, a critical aspect to enterprise use cases. The culmination of this effort is the release of a small, 7B, open-weight model that outperforms similarly sized peers in head-to-head comparisons and on Arabic-focused benchmarks covering cultural knowledge, instruction following, RAG, and contextual faithfulness.