ALLaM: Large Language Models for Arabic and English

Authors: M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, Nouf M. Alotaibi, Hisham Abdullah Alyahya, Sultan AlRashed, Faisal Abdulrahman Mirza, Shaykhah Z. Alsubaie, Hassan A. Alahmed, Ghadah Alabduljabbar, Raghad Alkhathran, Yousef Almushayqih, Raneem Alnajim, Salman Alsubaihi, Maryam Al Mansour, Saad Amin Hassan, Dr. Majed Alrubaian, Ali Alammari, Zaki Alawami, Abdulmohsen Al-Thubaity, Ahmed Abdelali, Jeril Kuriakose, Abdalghani Abujabal, Nora Al-Twairesh, Areeb Alowisheq, Haidar Khan
Primary area: foundation or frontier models, including LLMs
Keywords: Large Language Model, English, Arabic, Second Language Acquisition

Abstract:
In this work, 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 transferability of knowledge at scale. The models are based on an autoregressive decoder-only architecture and are pretrained on a mixture of Arabic and English texts. We illustrate how the second-language acquisition via vocabulary expansion can help steer a language model towards a new language without any major catastrophic forgetting in English. Furthermore, we highlight the effectiveness of using translation data and the process of knowledge encoding within the language model's latent space. Finally, we show that effective alignment with human preferences can significantly enhance the performance of a large language model (LLM) compared to less aligned models of a larger scale. Our methodology enables us to achieve 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 its base aligned models.

Decision: accept (poster)
Reviewer scores: 6, 3, 8, 8

Reviews and discussion:
<thread>
creation date:
1729822735190

thread_type:
official_review

<thread_object>
creator: reviewer_sgga

<content>
summary:
This paper describes the process for building ALLAM, an LLM trained on Arabic and English. Details about the training data and each stage of the training process are presented. Various evaluations are performed, including both automatic benchmarks like Arabic MMLU and human evaluation.

strengths:
This is a timely paper, as many research groups are working on building LLMs for languages other than English. The paper presents useful details on their experience and will serve as a good reference.

weaknesses:
There are no major weaknesses in my opinion. I would recommend adding discussing about other efforts in building LLMs for non-English languages and explaining how yours compare, i.e. moving some of the Related Work into the main paper. Especially it would be useful for the reader (who isn't referring to the appendix) to know about Jais and any other Arabic-centric model.

questions:
1. Page 3 says 4T English tokens then and 5.2T tokens for 30B pre-training. It was a bit confusing what is the 5.2T. Also Table 1 lists English as 660B tokens from mixed corpora -- is this a subset from the 4T English Only column?

2.  Fig 5/6: for my display, there are black lines in the legend (e.g. Arabic/English MMLU) but only red/orange lines in the figure.

3. I am curious if you have considered using something other than Llama2 as your base model. If so, why did you pick Llama2 in the end?
</content>
</thread_object>

</thread>
<thread>
creation date:
1730696436164

thread_type:
official_review

<thread_object>
creator: reviewer_9j4g

<content>
summary:
This paper presents a ALLaM: a family of large language models for Arabic and English, based on LLama-2 family of models.

The paper shows a method that can customize an existing LLM (like Llama) to acquire a much better performance at another language that was not included at all the original training of the model, without destroying the LLM's performance on previous language (i.e. second language acquisition).

The paper shows that second language acquisition can be done by augmenting the model in two major steps: 1-vocabulary expansion, and 2- carefully pre-training on mixture of new language and old language. This way the second language acquisition is not accompanied by catastrophic forgetting of the original language.

The paper includes a lot of experiments and comparisons with different baselines, including training from scratch. Showing that their second language acquisition method beats training from scratch. Many evaluations, both automated and human-evals are included in the paper.

strengths:
Originality: As far as I know, this is not the first work to fine-tune llama-based models for Arabic or other language acquisition. In my opinion, the main originality source in this paper might be the data curation effort, and the vocabulary expansion and continual pre-training without loss of original language. For example, the finding that initializing the embedding of the new tokens from averages of smaller tokens in the original tokenizer is an very useful finding and I expect to see it being used more in the future.

Quality: The effort spent in human evaluation and curation of the data is notable, and I hope this dataset gets released, at least partially for the research community to build on. The comparison against other Arabic-enabled LLMs are also very comprehensive and useful.

Clarity: While many typos and non-standard terminology exist and are highlighted in the weaknesses section. The paper is still mostly easy to follow and read. The description of the datasets and training/evaluation procedure is particularly clear and concise.

Significance: Arabic is definitely an excellent language choice for this exploration of second language acquisition, given that the original LLama2 family of models do not train on Arabic at all (as shown in Table 10 in the original Lama2 report), and that means that the tokenizer and model are not very well trained for Arabic. Also, Arabic is definitely an underserved language in the LLM, especially given the size of its native speakers population (fifth most in the world, roughly 400M people).

weaknesses:
Even though the paper is mostly well-written, it's littered with typos, non-standard terminology and missing references:
- "Fertility rate": very confusing term, it's not really a rate, and is usually referred to "fertility score": https://arxiv.org/pdf/2310.08754 
- Figure 8 is not referenced anywhere as far as I can see.
- line 475: missing reference
-  line 294: missing reference
- line 291: lower case "we"
- line 072: training of "these" 
- lines 151-153: DA identification is a hard task ... classifying the data to DA is not very difficult (opposite claim)  
- line 155: in-house in-house  

- I would have loved to see some newer Arabic models like Silma included in the comparisons for this paper, or a comparison against the models in the huggingface Arabic leaderboard.

questions:
- How many Arabic tokens already existed in the LLama2 backbone, and how many were added after the expansion?

- Why did the training mix change for the 34B model specifically? (lines 286-292) 

- I would be interested to compare allam against Silma Arabic llms

- Please consider evaluating on more dataset like AlGhafa benchmark or including ALLAM on the huggingface Arabic leaderboard.
</content>
</thread_object>

</thread>
<thread>
creation date:
1730865101854

thread_type:
official_review

<thread_object>
creator: reviewer_gnb6

<content>
summary:
This paper presents a series of LLMs for Arabic adapted from Llama2 as well as training from scratch.  The paper gives technical details of the pre-training, alignment and evaluation of these models.

strengths:
The paper gives the details of the training of Arabic LLMs, including training from scratch and contunue training from Llama2.  The result Arabic LLMs achieve SoTA on Arabic benchmarks.

weaknesses:
This is mainly a technical report rather than a research paper.  I do not see novel ideas or methods proposed.

questions:
none
</content>
</thread_object>

</thread>
<thread>
creation date:
1731132273351

thread_type:
official_review

<thread_object>
creator: reviewer_hsix

<content>
summary:
This paper introduces ALLAM, a large language model for Arabic and English. The authors demonstrate the effectiveness of their training and alignment strategy for achieving state-of-the-art performance on several Arabic benchmarks.

strengths:
Focus on Arabic Language: The paper specifically addresses the need for high-quality language models for Arabic, a language with a significant number of speakers worldwide and relatively fewer resources compared to English.  This focus fills a gap in the current landscape of large language models.    

Comprehensive Training and Alignment Strategy: The authors employ a thorough approach to training and aligning ALLAM. They experiment with different data mixtures, vocabulary expansion techniques, and use both supervised fine-tuning and preference training to achieve optimal performance.    

State-of-the-art Performance: ALLAM achieves state-of-the-art results on various Arabic benchmarks, demonstrating the effectiveness of the proposed techniques.  The authors also show that their model maintains or enhances English performance compared to the base Llama-2 model.    

Exploration of Second Language Acquisition: The paper investigates the use of second language acquisition techniques for LLMs, which is a promising area of research.  The authors show how pretraining on a mixture of Arabic and English text can lead to effective learning of Arabic without catastrophic forgetting in English.    

Detailed Methodology: The paper provides a detailed description of the training methodology, data curation process, and evaluation setup.  This transparency allows for reproducibility and facilitates future research in the field.   

Commitment to Openness: The authors express their intention to make the ALLAM models openly available to the community, which promotes collaboration and further development of Arabic language models.

weaknesses:
The authors should clearly state the originality of their work. While the paper builds on existing techniques such as second language acquisition for LLMs, it is not clear what specific novel components are being introduced. Is it the particular training recipe, the alignment strategy, or the focus on Arabic? A clear statement of originality is essential for establishing the contribution of this work.

Error Analysis:
The error analysis presented is currently insufficient. The authors need to go beyond simply stating that some evaluations provide more signal than others. A deeper dive into the types of errors made by the model on different benchmarks is necessary. What are the common patterns in incorrect predictions? Are there specific linguistic constructs or knowledge domains where the model struggles? Addressing these questions will provide valuable insights into the limitations of ALLAM and potential avenues for future research.


Additional Areas for Improvement:
Motivation for changing training data distribution: The authors mention changing the training data distribution for the 30B model. The motivation behind this change is not entirely clear and should be elaborated upon.
Fair comparison of models: The paper touches upon the difficulty of comparing models due to variations in training data size, architecture, and other factors. While comparing to larger models is a reasonable approach, the authors could consider additional metrics or analyses to further ensure a fair comparison.
Instruct model vs. base model results: The authors justify reporting results for the instruct model instead of the base model. However, providing some comparison or insights into the performance difference between the base and instruct models could be beneficial.

questions:
...
</content>
</thread_object>

</thread>
<thread>
creation date:
1731568435161

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Rebuttal Revision November 13 19:00 AoE

comment:
We sincerely thank the reviewers for their time and comprehensive reviews. We believe the reviews are general positive, highlighting the benefits of our novel methods (vocabulary expansion, embedding initialization, etc...) for second language acquisition, as well as our detailed training details that could benefit the training of similar models in the future. We have clarified the reviewer's questions and updated the manuscript on November 13 19:00 AoE according to their suggestions, which includes: 

1. Moving Figure 8 (now Figure 12) to the appendix, next to its reference.
2. Addressing typos and missed references mentioned by reviewer 9J4g.
3. Addressing other minor typos after further review.
4. Modifying appendix tables to have consistent style and remain within the manuscript’s margins.
5. Adding the penultimate introduction paragraph to mention similar Arabic-centric LLMs and reference the detailed descriptions in the appendix.
6. Modifying Table 1’s caption to clarify that the mixed English dataset is a subset of the English only dataset.
7. Adding new result analysis on "Why did we report Instruct model result instead of base model result in the main paper?" in the Appendix B
</content>
</thread_object>

</thread>
<thread>
creation date:
1732449097731

thread_type:
official_comment

<thread_object>
creator: authors

<content>
title:
Furthering Discussion

comment:
Seeing as the discussion deadline is approaching, we would appreciate any comments or insights to further the discussion and improve the manuscript.
</content>
</thread_object>

</thread>


Metareview:
metareview:
This paper presents the process used to train ALLaM, a series of Arabic and English LMs finetuned on top of Llama-2. The paper considers several adaption techniques to adapt the model to a new language and provides insights into best practices for adapting English LMs to other languages in the future. The resulting models achieve good performance on the considered downstream tasks.

Strengths:
- The paper is well-motivated (Arabic is a relatively underserved language among LLMs), and the paper clearly describes the process and methodology used to adapt an existing LM to a new language.
- Based on the experimental results, ALLaM models achieve state-of-the-art performance on multiple Arabic language benchmarks.

Weaknesses:
The main contribution of this paper is (and is stated to be) the development of a family of Arabic language models that achieve the state-of-the-art on Arabic language benchmarks.  However, limited artifacts from the paper have been released: the 7B and 13B models are only available through (separate) proprietary platforms (Appendix B), and there is no firm agreement to fully release the models upon publication. The data collected for Arabic pretraining, supervised finetuning (SFT), and preference optimization (DPO)—another major contribution, in my opinion—has also not been released. I encourage the authors to fully release their models and data, which will provide a significant contribution to the Arabic NLP community.

Some issues raised by the reviewers, such as presenting the novel aspects of the paper more clearly (Reviewer hSix) and the typos raised by 9J4g, have been addressed in the revised version of the paper.