Sailor: Open Language Models Tailored for South-East Asian Languages

Core Concepts

Sailor is a family of open language models ranging from 0.5B to 7B parameters, designed to perform well across South-East Asian languages including English, Chinese, Vietnamese, Thai, Indonesian, Malay, and Lao.

Abstract

The report presents the development of Sailor, a family of open language models tailored for South-East Asian (SEA) languages. Key insights and techniques used in the development process are discussed:

Data Preprocessing:

Merging adjacent short examples to reconstruct context
Employing document-level code-switching to improve multilingual performance
Aggressive data cleaning and deduplication to improve data quality

Tokenization:

Utilizing BPE Dropout to enhance model robustness against minor prompt variations

Training:

Tuning the learning rate to balance performance on English and SEA languages
Conducting data mixture simulation experiments to optimize the joint loss across all languages

The report also provides details on the data sources used, including high-quality English and Chinese datasets for replay, as well as datasets for SEA languages such as CC100, MADLAD-400, Wikipedia, and OpenSubtitles. The preprocessing pipeline, including data normalization, cleaning, and deduplication, is thoroughly described. Finally, the training infrastructure and details are outlined, highlighting the use of Megatron-LLM and TinyLlama codebases for efficient multi-GPU training.

Stats

The training data for Sailor models consists of 140B high-quality tokens for SEA languages and 60B tokens for replay (English and Chinese).
The effective tokens and equivalent epochs for each language and data source are provided in Table 3.

Quotes

None

Key Insights Distilled From

Sailor

by Longxu Dou,Q... at arxiv.org 04-05-2024

https://arxiv.org/pdf/2404.03608.pdf

Deeper Inquiries

How can the techniques developed for Sailor be applied to improve the performance of other multilingual language models beyond the SEA region?

The techniques developed for Sailor can be applied to enhance the performance of other multilingual language models beyond the SEA region by focusing on several key aspects:

Continual Pre-training: Continual pre-training is a crucial aspect of Sailor's approach. By continually pre-training models on a diverse range of languages and leveraging techniques like data mixture simulation and learning rate tuning, other multilingual models can benefit from improved language coverage and performance across various benchmarks.

Data Cleaning and Deduplication: Aggressive data cleaning and deduplication processes are essential for ensuring high-quality training data. By implementing robust data cleaning pipelines and deduplication strategies, other multilingual models can enhance their data quality, leading to better model performance.

Code-Switching Techniques: Document-level code-switching has shown promising results in improving model performance across multiple languages. By incorporating document-level code-switching and exploring the benefits of word-level code-switching, other multilingual models can better handle language mixing scenarios and improve their overall performance.

Tokenization Techniques: Techniques like BPE Dropout can enhance the robustness of language models by addressing issues related to subword segmentation. By extending BPE Dropout or combining it with other tokenization methods, such as Byte Pair Encoding (BPE) or SentencePiece, other multilingual models can improve their ability to handle diverse languages and tasks effectively.

Vocabulary Expansion: While vocabulary expansion has shown challenges in maintaining performance, further research and experimentation in this area could lead to improvements in vocabulary coverage for multilingual models. By exploring innovative approaches to vocabulary expansion and continual pre-training, other models can enhance their language capabilities.

Overall, by adopting and adapting the techniques developed for Sailor, other multilingual language models can benefit from improved data quality, robustness, and performance across a wide range of languages and tasks.

What are the potential challenges and limitations of the document-level code-switching approach, and how can it be further improved?

Document-level code-switching presents several challenges and limitations that need to be addressed for optimal performance:

Contextual Understanding: One challenge is ensuring that the model can accurately understand and interpret the context when languages are switched within a document. Maintaining coherence and consistency in the model's understanding across different languages is crucial for effective code-switching.

Data Quality: The quality of the code-switched data used for training can impact the model's performance. Noisy or low-quality code-switched text may introduce errors and hinder the model's ability to learn effectively. Improving data quality through rigorous cleaning and filtering processes is essential.

Language Imbalance: Document-level code-switching may lead to imbalances in the representation of different languages within the training data. Addressing language imbalance and ensuring equitable representation of all languages can help prevent bias and improve model performance.

Model Adaptation: Models need to adapt and generalize well to code-switched text in real-world scenarios. Ensuring that the model can effectively handle code-switching in various contexts and domains is crucial for practical applications.

To further improve document-level code-switching, the following strategies can be considered:

Fine-tuning: Fine-tuning the model on code-switched data from specific domains or tasks can help improve its performance in those contexts. Task-specific fine-tuning can enhance the model's ability to handle code-switching effectively.

Data Augmentation: Augmenting the training data with more diverse code-switched examples can help the model learn to handle a wider range of language mixing scenarios. Data augmentation techniques like back-translation and paraphrasing can be beneficial.

Multi-task Learning: Incorporating multi-task learning objectives that focus on code-switching tasks can help the model better understand and generate code-switched text. Training the model on tasks that require code-switching can improve its language mixing capabilities.

By addressing these challenges and implementing strategies for improvement, document-level code-switching can be enhanced to enable more effective multilingual language modeling.

Given the sensitivity of language models to subword segmentation, how can the BPE Dropout technique be extended or combined with other tokenization methods to enhance robustness across a wider range of languages and tasks?

BPE Dropout is a valuable technique for improving the robustness of language models by addressing issues related to subword segmentation. To enhance its effectiveness and extend its application across a wider range of languages and tasks, the following approaches can be considered:

Hybrid Tokenization: Combining BPE Dropout with other tokenization methods like SentencePiece or WordPiece can provide a more comprehensive approach to subword segmentation. By leveraging the strengths of different tokenization techniques, models can handle diverse languages and text types more effectively.

Adaptive Dropout Rates: Implementing adaptive dropout rates based on the complexity of the subword sequences can enhance the model's ability to handle varying linguistic structures. Adjusting the dropout rates dynamically during training can optimize the model's performance across different languages and tasks.

Language-Specific Dropout: Tailoring the dropout rates based on the characteristics of specific languages can improve the model's robustness for multilingual applications. Language-specific dropout strategies can account for the unique subword patterns and structures present in different languages.

Task-Specific Dropout: Adapting the dropout rates according to the requirements of specific tasks can optimize the model's performance for diverse applications. Task-specific dropout tuning can enhance the model's ability to handle different types of linguistic input and tasks effectively.

Ensemble Methods: Employing ensemble methods that combine models trained with different dropout rates can enhance the overall robustness and generalization of the language model. Ensemble techniques can leverage the diversity of models to improve performance across a wider range of languages and tasks.

By exploring these strategies and combining BPE Dropout with other tokenization methods and adaptive techniques, language models can achieve greater robustness and effectiveness in handling diverse linguistic data and tasks.