Summary of Allam: Large Language Models For Arabic and English, by M Saiful Bari et al.

ALLaM: Large Language Models for Arabic and English

by M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, Nouf M. Alotaibi, Hisham A. Alyahya, Sultan AlRashed, Faisal A. Mirza, Shaykhah Z. Alsubaie, Hassan A. Alahmed, Ghadah Alabduljabbar, Raghad Alkhathran, Yousef Almushayqih, Raneem Alnajim, Salman Alsubaihi, Maryam Al Mansour, Majed Alrubaian, Ali Alammari, Zaki Alawami, Abdulmohsen Al-Thubaity, Ahmed Abdelali, Jeril Kuriakose, Abdalghani Abujabal, Nora Al-Twairesh, Areeb Alowisheq, Haidar Khan

First submitted to arxiv on: 22 Jul 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The proposed ALLaM (Arabic Large Language Model) is a series of large language models designed to support the ecosystem of Arabic Language Technologies. The model’s autoregressive decoder-only architecture demonstrates how vocabulary expansion and pretraining on mixed Arabic-English text can steer it towards Arabic without forgetting English. The paper also highlights the effectiveness of parallel/translated data in aligning languages and shows that extensive alignment with human preferences enhances performance. ALLaM achieves state-of-the-art results in various Arabic benchmarks, including MMLU Arabic, ACVA, and Arabic Exams.
Low	GrooveSquid.com (original content)	Low Difficulty Summary The paper creates a new language model called ALLaM to help people who work with Arabic languages. They make sure the model is trained properly so it doesn’t forget what it already knows about English. The model also gets better at understanding Arabic by using both Arabic and English texts. This helps the model learn more words and become better at speaking. The paper shows that making sure the model understands human language makes it do even better.