AURORA-M: A Multilingual Open-Source Language Model Trained with Safety and Compliance in Mind

Core Concepts

AURORA-M is a 15B parameter multilingual open-source language model that addresses key challenges in existing models, including limited multilingual capabilities, catastrophic forgetting, and lack of compliance with AI safety and development laws. It is the first open-source multilingual model fine-tuned on human-reviewed safety instructions to align its development with the Biden-Harris Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.

Abstract

The paper introduces AURORA-M, a new 15B parameter multilingual open-source language model that aims to address several limitations in existing open-source language models.
The key highlights are:

AURORA-M is developed using a two-stage continual pretraining approach, consisting of Continual Auxiliary Pretraining (CAP) and Continual Alignment Tuning (CAT). This approach is designed to maximize adaptation, minimize catastrophic forgetting, and align AURORA-M with safety objectives.

AURORA-M is trained on a diverse dataset of 435 billion tokens, covering six linguistically diverse languages: English, Finnish, Hindi, Japanese, Vietnamese, and code. This extensive training data enables AURORA-M to demonstrate robust performance across various tasks and languages.

AURORA-M is the first open-source multilingual model that has been fine-tuned on a comprehensive collection of human-reviewed safety instructions, addressing concerns outlined in the Biden-Harris Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence. This fine-tuning process aligns AURORA-M's development with both conventional red-teaming considerations and the specific safety and security guidelines articulated in the Order.

The paper presents a rigorous evaluation of AURORA-M across a diverse spectrum of tasks and languages, demonstrating its superior performance in multilingual settings while retaining competitive performance in English and coding tasks. The authors also construct a new red-teaming dataset, "The Biden-Harris Redteam Dataset," to evaluate AURORA-M's safety and compliance.

The authors show the influence of scaling the total training tokens on various multilingual and code evaluation tasks, highlighting the benefits of the extensive training data used for AURORA-M.

Stats

AURORA-M is trained on a total of 435 billion additional tokens, resulting in a total training token count of over 2 trillion tokens.
The training data consists of 377 billion tokens in the Continual Auxiliary Pretraining (CAP) stage and 58 billion tokens in the Continual Alignment Tuning (CAT) stage.
The data includes web data from sources like Stack, RefinedWeb, RedPajama, and a subset of the Pile, as well as multilingual data from HPLT, MC4, Paracrawl, OSCAR, Wikipedia, and instruction tuning data from sources like OpenAssistant, APIBench, and OIG.
The authors also introduced a new safety instructions dataset named "Biden-Harris Redteam" to fine-tune AURORA-M on safety and compliance.

Quotes

"AURORA-M is the first open-source multilingual model fine-tuned on human-reviewed safety instructions, thus aligning its development not only with conventional red-teaming considerations, but also with the specific concerns articulated in the Biden-Harris Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence."
"To comprehensively evaluate AURORA-M's efficacy, we conduct a rigorous examination across a diverse spectrum of tasks spanning various domains and languages. Our evaluations aim to gauge AURORA-M's capacity to retain previously learned knowledge while acquiring new capabilities through continual pretraining."

Key Insights Distilled From

Aurora-M

by Taishi Nakam... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00399.pdf

Deeper Inquiries

How can the two-stage continual pretraining approach used in AURORA-M be extended to other types of large language models, such as multimodal or task-specific models, to improve their safety and compliance?

The two-stage continual pretraining approach employed in AURORA-M can be extended to other types of large language models, such as multimodal or task-specific models, to enhance their safety and compliance in several ways:

Multimodal Models: For multimodal models that combine text with other modalities like images or audio, the two-stage continual pretraining approach can be adapted to incorporate diverse multimodal datasets in the pretraining stages. This would involve exposing the model to a wide range of multimodal data sources during the Continual Auxiliary Pretraining (CAP) stage and fine-tuning on specific multimodal tasks during the Continual Alignment Tuning (CAT) stage. By continually updating the model with new multimodal data distributions, it can adapt to evolving multimodal contexts while maintaining safety and compliance standards.

Task-Specific Models: Task-specific models designed for specialized applications can benefit from the two-stage continual pretraining approach by tailoring the pretraining data and fine-tuning tasks to the specific domain requirements. In the CAP stage, the model can be exposed to a diverse range of data relevant to the target task, ensuring a broad understanding of the domain. The CAT stage can then focus on fine-tuning the model on task-specific datasets and safety guidelines to ensure compliance with regulatory standards.

Safety and Compliance Modules: To further enhance safety and compliance in large language models, additional modules can be integrated into the two-stage pretraining process. These modules could include mechanisms for detecting and mitigating harmful content, bias detection and mitigation strategies, and privacy-preserving techniques. By incorporating these modules into the continual pretraining pipeline, models can be equipped to handle safety and compliance challenges proactively.

Cross-Lingual and Cross-Modal Alignment: Extending the two-stage pretraining approach to support cross-lingual and cross-modal alignment can improve the model's ability to generalize across different languages and modalities while maintaining safety and compliance standards. By incorporating alignment tasks in both pretraining stages, models can learn to transfer knowledge effectively and ensure consistent performance across diverse contexts.

In summary, extending the two-stage continual pretraining approach to multimodal and task-specific models involves customizing the pretraining data, fine-tuning tasks, and incorporating safety and compliance modules to enhance the models' adaptability and robustness in various applications.

How could the two-stage continual pretraining approach used in AURORA-M be extended to other types of large language models, such as multimodal or task-specific models, to improve their safety and compliance?

The two-stage continual pretraining approach employed in AURORA-M can be extended to other types of large language models, such as multimodal or task-specific models, to enhance their safety and compliance in several ways:

Multimodal Models: For multimodal models that combine text with other modalities like images or audio, the two-stage continual pretraining approach can be adapted to incorporate diverse multimodal datasets in the pretraining stages. This would involve exposing the model to a wide range of multimodal data sources during the Continual Auxiliary Pretraining (CAP) stage and fine-tuning on specific multimodal tasks during the Continual Alignment Tuning (CAT) stage. By continually updating the model with new multimodal data distributions, it can adapt to evolving multimodal contexts while maintaining safety and compliance standards.

Task-Specific Models: Task-specific models designed for specialized applications can benefit from the two-stage continual pretraining approach by tailoring the pretraining data and fine-tuning tasks to the specific domain requirements. In the CAP stage, the model can be exposed to a diverse range of data relevant to the target task, ensuring a broad understanding of the domain. The CAT stage can then focus on fine-tuning the model on task-specific datasets and safety guidelines to ensure compliance with regulatory standards.

Safety and Compliance Modules: To further enhance safety and compliance in large language models, additional modules can be integrated into the two-stage pretraining process. These modules could include mechanisms for detecting and mitigating harmful content, bias detection and mitigation strategies, and privacy-preserving techniques. By incorporating these modules into the continual pretraining pipeline, models can be equipped to handle safety and compliance challenges proactively.

Cross-Lingual and Cross-Modal Alignment: Extending the two-stage pretraining approach to support cross-lingual and cross-modal alignment can improve the model's ability to generalize across different languages and modalities while maintaining safety and compliance standards. By incorporating alignment tasks in both pretraining stages, models can learn to transfer knowledge effectively and ensure consistent performance across diverse contexts.

In summary, extending the two-stage continual pretraining approach to multimodal and task-specific models involves customizing the pretraining data, fine-tuning tasks, and incorporating safety and compliance modules to enhance the models' adaptability and robustness in various applications.

Given the focus on multilingual capabilities, how could AURORA-M be leveraged to support the development of high-quality machine translation systems for low-resource language pairs, and what additional challenges would need to be addressed?

AURORA-M's multilingual capabilities can be leveraged to support the development of high-quality machine translation systems for low-resource language pairs by following these strategies:

Transfer Learning: AURORA-M can serve as a strong foundation for transfer learning in machine translation. By fine-tuning the model on parallel corpora of low-resource language pairs, it can learn to generate accurate translations for these languages. The pretraining on diverse languages in AURORA-M provides a solid base for transfer learning to low-resource languages.

Data Augmentation: AURORA-M can be used to augment the training data for low-resource languages by generating synthetic data through back-translation. This technique involves translating monolingual data from the low-resource language to a high-resource language and then back to the low-resource language. By leveraging AURORA-M's multilingual capabilities, this process can generate additional training data to improve translation quality.

Domain Adaptation: AURORA-M can be fine-tuned on domain-specific data for low-resource languages to improve translation quality in specialized domains. By training the model on domain-specific parallel corpora, it can learn to produce more accurate translations in specific domains such as legal, medical, or technical fields.

Challenges: Despite the benefits, leveraging AURORA-M for low-resource machine translation poses several challenges. These include the availability of high-quality parallel corpora for low-resource languages, domain adaptation for specialized translations, handling language-specific nuances and idiomatic expressions, and ensuring the model's robustness to variations in dialects and linguistic styles within the low-resource language.

Evaluation and Fine-Tuning: Rigorous evaluation and fine-tuning of AURORA-M on low-resource language pairs are essential to ensure the model's performance meets the desired quality standards. Continuous monitoring and improvement based on feedback from native speakers and domain experts are crucial for enhancing translation accuracy and fluency.

In conclusion, AURORA-M's multilingual capabilities offer a promising foundation for developing high-quality machine translation systems for low-resource languages, but addressing data availability, domain adaptation, linguistic nuances, and robust evaluation are key challenges that need to be overcome for successful implementation.