Release of Pre-Trained Models for the Japanese Language
Kei Sawada, Tianyu Zhao, Makoto Shing, Kentaro Mitsui, Akio Kaga, Yukiya Hono, Toshiaki Wakatsuki, Koh Mitsuda
TL;DR
This work addresses the language bias in AI democratization by releasing Japanese-pretrained, culturally aligned versions of GPT, CLIP, Stable Diffusion, and HuBERT on Hugging Face. It demonstrates that language-specific pre-training, data curation, and targeted fine-tuning yield strong performance on Japanese tasks across text, image, and speech modalities. The results show competitive or superior performance relative to English-centric baselines, while enmeshing Japanese cultural identity into model outputs. Overall, the release framework and curated data significantly lower barriers to AI access for Japanese users and set a precedent for language-tailored model democratization with broad practical impact.
Abstract
AI democratization aims to create a world in which the average person can utilize AI techniques. To achieve this goal, numerous research institutes have attempted to make their results accessible to the public. In particular, large pre-trained models trained on large-scale data have shown unprecedented potential, and their release has had a significant impact. However, most of the released models specialize in the English language, and thus, AI democratization in non-English-speaking communities is lagging significantly. To reduce this gap in AI access, we released Generative Pre-trained Transformer (GPT), Contrastive Language and Image Pre-training (CLIP), Stable Diffusion, and Hidden-unit Bidirectional Encoder Representations from Transformers (HuBERT) pre-trained in Japanese. By providing these models, users can freely interface with AI that aligns with Japanese cultural values and ensures the identity of Japanese culture, thus enhancing the democratization of AI. Additionally, experiments showed that pre-trained models specialized for Japanese can efficiently achieve high performance in Japanese tasks.