Enhancing Japanese Language Capabilities of Large Language Models through Continual Pre-Training

Core Concepts

Continual pre-training of large language models initially trained on English corpora can effectively enhance their Japanese language capabilities, outperforming Japanese language models trained from scratch.

Abstract

This study presents the development of Swallow, a large language model (LLM) with enhanced Japanese capabilities, through continual pre-training on Llama 2 models. The key insights are:

Continual pre-training on Japanese corpora significantly improves the performance of Llama 2 models on Japanese tasks, especially in question answering, by up to 70%. The performance monotonically increases with the amount of Japanese training data.

Swallow, the continually pre-trained model, outperforms Japanese LLMs trained from scratch by 8.4 to 17.4 points on average, demonstrating the efficiency of the cross-lingual continual pre-training approach.

Vocabulary expansion, which adds Japanese characters and words to the model's vocabulary, improves computational efficiency without negatively impacting performance, except for the summarization task.

Incorporating parallel Japanese-English corpora into the continual pre-training enhances translation performance, particularly from Japanese to English, without degrading performance on other tasks.

The study provides valuable insights into effective methodologies for cross-lingual adaptation of large language models, leveraging both English and Japanese language resources.

Stats

The Swallow Corpus contains approximately 100B tokens, with 90% Japanese text and 10% English text.
The Japanese Wikipedia corpus contains 1.6B tokens.
The vocabulary expansion added 11,176 subwords to the original LLaMA tokenizer, resulting in a total vocabulary size of 43,176.

Quotes

"Continual pre-training of large language models (LLMs) initially trained on English corpus allows us to leverage the vast amount of English language resources and reduce the pre-training cost."
"Swallow achieved the highest performance in Japanese among all models developed in Japan (as of December 2023)."
"We show that continual pre-training is effective for improving Japanese abilities, especially question answering tasks that require Japanese knowledge."

Key Insights Distilled From

Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities

by Kazuki Fujii... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.17790.pdf

Deeper Inquiries

How can the cross-lingual transfer capabilities of continually pre-trained models be further improved beyond translation tasks?

Continual pre-training on large language models (LLMs) offers a promising avenue for enhancing cross-lingual transfer capabilities beyond translation tasks. To further improve these capabilities, several strategies can be considered:

Task Diversification: Incorporating a diverse set of tasks during continual pre-training can help the model learn a wide range of linguistic features and nuances across languages. Tasks such as natural language inference, sentiment analysis, and named entity recognition can expose the model to different aspects of language understanding.

Multilingual Knowledge Distillation: Leveraging knowledge distillation techniques, where a large pre-trained model transfers its knowledge to a smaller model, can help distill cross-lingual knowledge effectively. By distilling the knowledge learned during continual pre-training into a smaller model, the cross-lingual transfer capabilities can be enhanced.

Fine-Tuning on Language-Specific Data: After continual pre-training, fine-tuning the model on language-specific datasets can further refine its understanding of individual languages. Fine-tuning on diverse datasets in each language of interest can help the model adapt to specific linguistic characteristics and improve performance on language-specific tasks.

Incorporating Multimodal Data: Integrating multimodal data, such as images and audio, during pre-training can enhance the model's understanding of context and improve its cross-lingual transfer capabilities. Multimodal pre-training can enable the model to learn associations between different modalities and languages, leading to more robust cross-lingual performance.

By implementing these strategies, continually pre-trained models can be equipped with enhanced cross-lingual transfer capabilities beyond traditional translation tasks, enabling them to excel in a variety of language-related applications.

What are the potential limitations or biases introduced by the predominant use of web-crawled data in the training corpora?

The predominant use of web-crawled data in training corpora can introduce several limitations and biases that need to be considered:

Quality and Reliability: Web-crawled data may contain noise, errors, and inaccuracies, leading to lower data quality and reliability. Biased or misleading information present on the web can negatively impact the model's learning process and performance.

Domain Specificity: Web-crawled data may not cover all domains equally, leading to biases towards certain topics or domains. This can result in models that are proficient in specific domains but lack generalizability across diverse domains.

Representation Bias: Web-crawled data may not be representative of the entire population, leading to biases in the learned representations. Underrepresented or marginalized groups may not be adequately represented in the training data, leading to biased model predictions and outputs.

Privacy Concerns: Web-crawled data may contain personal or sensitive information, raising privacy concerns. Models trained on such data may inadvertently learn and reproduce sensitive information, compromising user privacy.

Language and Cultural Biases: Web-crawled data may reflect the biases and perspectives prevalent on the web, leading to language and cultural biases in the trained models. This can result in models that exhibit biases towards certain languages or cultures.

Addressing these limitations and biases requires careful data curation, diverse dataset selection, bias mitigation techniques, and ethical considerations throughout the model development process.

Could the insights from this study on cross-lingual adaptation be applied to other language pairs beyond English and Japanese?

The insights gained from this study on cross-lingual adaptation, particularly through continual pre-training, can indeed be applied to other language pairs beyond English and Japanese. Here are some ways in which these insights can be extended to other languages:

Transfer Learning Framework: The methodology of continual pre-training and fine-tuning can be applied to other language pairs by adapting the pre-training data and tasks to the specific languages of interest. By leveraging a similar framework, models can be adapted to new languages efficiently.

Vocabulary Expansion Techniques: The vocabulary expansion techniques explored in the study can be adapted to incorporate the linguistic characteristics of other languages. By expanding the vocabulary to include language-specific characters and words, models can improve their efficiency and performance in new languages.

Parallel Corpus Integration: The effectiveness of incorporating parallel corpora for cross-lingual transfer can be extended to other language pairs. By utilizing parallel data in training, models can enhance their translation capabilities and improve cross-lingual transfer performance for diverse language pairs.

Task-Specific Adaptation: Insights on task-specific improvements through continual pre-training can guide the adaptation of models to new languages for specific tasks. By identifying the most effective tasks for cross-lingual transfer, models can be optimized for performance in various linguistic tasks across different language pairs.

By applying the principles and methodologies of cross-lingual adaptation from this study to other language pairs, researchers can advance the development of multilingual models with improved cross-lingual capabilities and performance.