# ALLaM: Large Language Models for Arabic and English

arXiv: https://arxiv.org/abs/2407.15390 · HTML: https://mlsanity.com/paper/2407.15390/

## Key facts

- Cited by 55 papers in the map (data through 14 Oct 2026).
- Builds on 72 of its 111 references that are in the map.
- First posted on arXiv 22 Jul 2024.

## Builds on (most cited first, 30 of 72)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [PIQA: Reasoning about Physical Commonsense in Natural Language](https://mlsanity.com/paper/1911.11641/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning](https://mlsanity.com/paper/2307.08691/) (2023)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions](https://mlsanity.com/paper/1905.10044/) (2019)
- [MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://mlsanity.com/paper/2406.01574/) (2024)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://mlsanity.com/paper/2210.09261/) (2022)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)

## Built on by (most cited first, 30 of 55)

- [Large Concept Models: Language Modeling in a Sentence Representation Space](https://mlsanity.com/paper/2412.08821/) (2024)
- [Commonsense Reasoning in Arab Culture](https://mlsanity.com/paper/2502.12788/) (2025)
- [Command R7B Arabic: A Small, Enterprise Focused, Multilingual, and Culturally Aware Arabic LLM](https://mlsanity.com/paper/2503.14603/) (2025)
- [Atlas-Chat: Adapting Large Language Models for Low-Resource Moroccan Arabic Dialect](https://mlsanity.com/paper/2409.17912/) (2024)
- [A Survey of Large Language Models for Arabic Language and its Dialects](https://mlsanity.com/paper/2410.20238/) (2024)
- [NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities](https://mlsanity.com/paper/2505.18383/) (2025)
- [Lost in the Mix: Evaluating LLM Understanding of Code-Switched Text](https://mlsanity.com/paper/2506.14012/) (2025)
- [Assessing Large Language Models on Islamic Legal Reasoning: Evidence from Inheritance Law Evaluation](https://mlsanity.com/paper/2509.01081/) (2025)
- [SpokenNativQA: Multilingual Everyday Spoken Queries for LLMs](https://mlsanity.com/paper/2505.19163/) (2025)
- [DialectalArabicMMLU: Benchmarking Dialectal Capabilities in Arabic and Multilingual Language Models](https://mlsanity.com/paper/2510.27543/) (2025)
- [Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale](https://mlsanity.com/paper/2509.14008/) (2025)
- [Large Language Models and Arabic Content: A Review](https://mlsanity.com/paper/2505.08004/) (2025)
- [QU-NLP at QIAS 2025 Shared Task: A Two-Phase LLM Fine-Tuning and Retrieval-Augmented Generation Approach for Islamic Inheritance Reasoning](https://mlsanity.com/paper/2508.15854/) (2025)
- [Kuwain 1.5B: An Arabic SLM via Language Injection](https://mlsanity.com/paper/2504.15120/) (2025)
- [The Arabic AI Fingerprint: Stylometric Analysis and Detection of Large Language Models Text](https://mlsanity.com/paper/2505.23276/) (2025)
- [Beyond MCQ: An Open-Ended Arabic Cultural QA Benchmark with Dialect Variants](https://mlsanity.com/paper/2510.24328/) (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)
- [Reinforcement Learning with Semantic Rewards Enables Low-Resource Language Expansion without Alignment Tax](https://mlsanity.com/paper/2605.14366/) (2026)
- [Helping Large Language Models Protect Themselves: An Enhanced Filtering and Summarization System](https://mlsanity.com/paper/2505.01315/) (2025)
- [Mutarjim: Advancing Bidirectional Arabic-English Translation with a Small Language Model](https://mlsanity.com/paper/2505.17894/) (2025)
- [Are LLMs Good Text Diacritizers? An Arabic and Yoruba Case Study](https://mlsanity.com/paper/2506.11602/) (2025)
- [From Words to Proverbs: Evaluating LLMs Linguistic and Cultural Competence in Saudi Dialects with Absher](https://mlsanity.com/paper/2507.10216/) (2025)
- [Saudi-Dialect-ALLaM: LoRA Fine-Tuning for Dialectal Arabic Generation](https://mlsanity.com/paper/2508.13525/) (2025)
- [UI-Level Evaluation of ALLaM 34B: Measuring an Arabic-Centric LLM via HUMAIN Chat](https://mlsanity.com/paper/2508.17378/) (2025)
- [AraLingBench A Human-Annotated Benchmark for Evaluating Arabic Linguistic Capabilities of Large Language Models](https://mlsanity.com/paper/2511.14295/) (2025)
- [IDIOLEX: Unified and Continuous Representations for Idiolectal and Stylistic Variation](https://mlsanity.com/paper/2604.04704/) (2026)
- [SAHM: A Benchmark for Arabic Financial and Shari'ah-Compliant Reasoning](https://mlsanity.com/paper/2604.19098/) (2026)
- [HalluScore: Large Language Model Hallucination Question Answering Benchmark](https://mlsanity.com/paper/2605.17007/) (2026)
- [Arabic Stable LM: Adapting Stable LM 2 1.6B to Arabic](https://mlsanity.com/paper/2412.04277/) (2024)

## Abstract (arXiv)

We present ALLaM: Arabic Large Language Model, a series of large language models to support the ecosystem of Arabic Language Technologies (ALT). ALLaM is carefully trained considering the values of language alignment and knowledge transfer at scale. Our autoregressive decoder-only architecture models demonstrate how second-language acquisition via vocabulary expansion and pretraining on a mixture of Arabic and English text can steer a model towards a new language (Arabic) without any catastrophic forgetting in the original language (English). Furthermore, we highlight the effectiveness of using parallel/translated data to aid the process of knowledge alignment between languages. Finally, we show that extensive alignment with human preferences can significantly enhance the performance of a language model compared to models of a larger scale with lower quality alignment. ALLaM achieves state-of-the-art performance in various Arabic benchmarks, including MMLU Arabic, ACVA, and Arabic Exams. Our aligned models improve both in Arabic and English from their base aligned models.