The reviewed record of science sign in
Pith

arxiv: 2303.04715 · v2 · pith:AM7QWWRX · submitted 2023-03-08 · cs.CL · cs.AI

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

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:AM7QWWRXrecord.jsonopen to challenge →

classification cs.CL cs.AI
keywords bloom-zhchinesemodelstraditionalbloomenglishlanguagepre-training
0
0 comments X
read the original abstract

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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.