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

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

classification cs.CLcs.AI
keywords bloom-zhchinesemodelstraditionalbloomenglishlanguagepre-training
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese

    cs.CL 2025-05 accept novelty 7.0 of 10

    A new benchmark shows LLMs are more accurate in Simplified Chinese for regional terms but favor Taiwanese names in simulated hiring, revealing task-dependent bias between Chinese script variants.

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