mlsanityData to 14 Oct 2026

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

arXiv 2309.13079 ↗First posted 21 Sep 2023cs.CL cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 9 of its 12 references that are in the map. First posted on arXiv 21 Sep 2023.

Builds on

9 of 12 references.

References · 9

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023FinGPT: Open-Source Financial Large Language Modelscited by 253
  3. 2023Simple synthetic data reduces sycophancy in large language modelscited by 187
  4. 2023DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Taskcited by 131
  5. 2023Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecturecited by 122
  6. 2023Lawyer LLaMA Technical Reportcited by 54
  7. 2023WanJuan: A Comprehensive Multimodal Dataset for Advancing English and Chinese Large Modelscited by 50
  8. 2023A Brief Report on LawGPT 1.0: A Virtual Legal Assistant Based on GPT-3cited by 46
  9. 2023Don't Ignore Dual Logic Ability of LLMs while Privatizing: A Data-Intensive Analysis in Medical Domaincited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.