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MiChao-HuaFen 1.0: A Specialized Pre-trained Corpus Dataset for Domain-specific Large Models

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arxiv 2309.13079 v2 pith:X4XJQWCG submitted 2023-09-21 cs.CL cs.AI

Yidong Liu , FuKai Shang , Fang Wang , Rui Xu , Jun Wang , Wei Li , Yao Li , Conghui He This is my paper · ORCID
classification cs.CLcs.AI
keywords datasetdomainslargemodelscorpusdeepdomain-specificlearning
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With the advancement of deep learning technologies, general-purpose large models such as GPT-4 have demonstrated exceptional capabilities across various domains. Nevertheless, there remains a demand for high-quality, domain-specific outputs in areas like healthcare, law, and finance. This paper first evaluates the existing large models for specialized domains and discusses their limitations. To cater to the specific needs of certain domains, we introduce the ``MiChao-HuaFen 1.0'' pre-trained corpus dataset, tailored for the news and governmental sectors. The dataset, sourced from publicly available internet data from 2022, underwent multiple rounds of cleansing and processing to ensure high quality and reliable origins, with provisions for consistent and stable updates. This dataset not only supports the pre-training of large models for Chinese vertical domains but also aids in propelling deep learning research and applications in related fields.

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Cited by 2 Pith papers

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  1. Ultra-FineWeb: Efficient Data Filtering and Verification for High-Quality LLM Training Data

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    Ultra-FineWeb is a fastText-filtered pretraining corpus whose seed samples were chosen by a cheap 'efficient verification' step, and 1.2B models trained on it outperform models trained on FineWeb and FineWeb-edu on av...

  2. OpenCSG Chinese Corpus: A Series of High-quality Chinese Datasets for LLM Training

    cs.CL 2025-01 conditional novelty 5.0 of 10

    OpenCSG released four open Chinese LLM training datasets, and 2B-scale tests report improved C-Eval, CMMLU, and Alignbench scores.

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