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ALLaM: Large Language Models for Arabic and English

Summary

The paper addresses the lack of strong Arabic LLMs. Frontier models are mostly English-centric, and Arabic pretraining data is scarce. The NCAI/SDAIA team builds ALLaM in two stages. First, they continue training Llama-2 checkpoints after expanding the tokenizer vocabulary with Arabic tokens. Then they apply what they learned to a 7B model trained from random initialization: English-only pretraining first, followed by a mixed Arabic–English phase. The Arabic corpus combines crawled web pages, news, books, Wikipedia and audio transcripts with filtering and deduplication. They also machine-translate English corpora into Arabic to tie the two languages together. After pretraining, the models receive supervised fine-tuning and preference training. They are evaluated with automatic benchmarks, LLM judges and human raters. ALLaM reaches state-of-the-art results on Arabic benchmarks such as MMLU Arabic, ACVA and Arabic Exams, and its English scores improve over the original Llama-2. This shows that a new language can be added to an open model without catastrophic forgetting. Such a path is cheaper and greener than training every regional model from scratch, which matters for under-served languages.

Key points

  • The release has four models: 7B, 13B and 70B initialized from Llama-2 weights, plus a 7B model trained from scratch, which beats the 7B continued-pretraining variant.
  • English data comes to about 4T high- and medium-quality tokens, drawn from Dolma-v1 and Pile subsets (Dolma-CC, The Stack, PeS2o, PubMed, DM-Math, StackExchange).
  • Arabic web data is cleaned with four filters:
    • removing documents with a language-ID score under 95%;
    • removing documents shorter than 30 words;
    • removing duplicate URLs and spam- or stopword-heavy pages;
    • exact-match deduplication. Fuzzy deduplication was rejected because it removed too much of the scarce Arabic data.
  • Dolma's Wikipedia, books, C4 and peS2o were translated into Arabic with an in-house MT system. A language-ratio ablation settled on about 55% English in the mix.
  • Tokenizer fertility is compared across four tokenizers on English, Arabic and code: Llama-2, ALLaM Arabic-only, ALLaM merged with Llama-2, and ALLaM from scratch.

Interesting ideas

  • I1. Prototype cheaply before training from scratch. The team ran its ablations in the cheaper continued-pretraining regime and only then committed to a full from-scratch run. Any group with limited compute, including Ukrainian-language projects, could de-risk expensive decisions the same way.
  • I2. Translated data as a cross-lingual bridge. Adding machine-translated versions of corpora already present in the source language made the target language align better with it. It also reduced forgetting of the source language and produced fewer gradient-norm spikes, even though training loss was higher. A low-resource language could borrow this recipe directly.
  • I3. Lower loss is not always the better run. In the translation ablation, the run with higher loss gave the more stable and more balanced model. Tracking gradient-norm spikes and per-language benchmarks alongside the loss gives a fuller picture of a training run.
  • I4. Tune deduplication strictness to how much data you have. Exact-match deduplication was kept and fuzzy matching dropped because the latter discarded too much Arabic text. Filtering choices should be calibrated for each language rather than copied from English pipelines.
  • I5. Measure tokenizer fertility per language and domain. Comparing tokens-per-word on English, target-language and code samples is a simple diagnostic. It shows whether vocabulary expansion is worth doing before any model training starts.

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: Linguistics & languages

Cited by

109 papers of our library cite it (of the 140673 whose bibliographies we read); 6 of them have a page here, 103 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2407.15390 · arXiv 2407.15390

@misc{bari2024allam,
  title = {{ALLaM: Large Language Models for Arabic and English}},
  author = {Bari, M Saiful and Alnumay, Yazeed and Alzahrani, Norah A. and Alotaibi, Nouf M. and Alyahya, Hisham A. and AlRashed, Sultan and Mirza, Faisal A. and Alsubaie, Shaykhah Z. and Alahmed, Hassan A. and Alabduljabbar, Ghadah and Alkhathran, Raghad and Almushayqih, Yousef and Alnajim, Raneem and Alsubaihi, Salman and Mansour, Maryam Al and Alrubaian, Majed and Alammari, Ali and Alawami, Zaki and Al-Thubaity, Abdulmohsen and Abdelali, Ahmed and Kuriakose, Jeril and Abujabal, Abdalghani and Al-Twairesh, Nora and Alowisheq, Areeb and Khan, Haidar},
  year = {2024},
  doi = {10.48550/arxiv.2407.15390},
  eprint = {2407.15390},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2407.15390}
}

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