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Extending the Pre-Training of BLOOM for Improved Support of Traditional Chinese: Models, Methods and Results

2023-03-08 · Philipp Ennen, Po-chun Hsu, Chan-Jan Hsu, Chang-Le Liu, Yen-chen Wu, Yin-Hsiang Liao, Chin-Tung Lin, Da-Shan Shiu, Wei-Yun Ma

In this paper we present the multilingual language model BLOOM-zh that features enhanced support for Traditional Chinese. BLOOM-zh has its origins in the open-source BLOOM models presented by BigScience in 2022. Starting from released models, we extended the pre-training of BLOOM by additional 7.4 billion tokens in Traditional Chinese and English covering a variety of domains such as news articles, books, encyclopedias, educational materials as well as spoken language. In order to show the properties of BLOOM-zh, both existing and newly created benchmark scenarios are used for evaluating the performance. BLOOM-zh outperforms its predecessor on most Traditional Chinese benchmarks while maintaining its English capability. We release all our models to the research community.

📄 PDF Abstract BibTeX arXiv:2303.04715

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ArticlesLanguage ModelingLanguage Modelling

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BLOOM BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages…

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