SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanities

Liu Chang, Wang Dongbo, Zhao Zhixiao, Hu Die, Wu Mengcheng +6 more
4/16/2023
cs.CL

No official code found. Best implementation: hsc748NLP/SikuGPT (21 stars, last commit 2023-04-30).

Abstract

The rapid advance in artificial intelligence technology has facilitated the prosperity of digital humanities research. Against such backdrop, research methods need to be transformed in the intelligent processing of ancient texts, which is a crucial component of digital humanities research, so as to adapt to new development trends in the wave of AIGC. In this study, we propose a GPT model called SikuGPT based on the corpus of Siku Quanshu. The model's performance in tasks such as intralingual translation and text classification exceeds that of other GPT-type models aimed at processing ancient texts. SikuGPT's ability to process traditional Chinese ancient texts can help promote the organization of ancient information and knowledge services, as well as the international dissemination of Chinese ancient culture.

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Code Implementations(1)

hsc748NLP/SikuGPTCommunity100%
210Feb 24, 20233 years ago

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Cite this paper

@article{chang2023sikugpt,
  title  = {SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanities},
  author = {Liu Chang and Wang Dongbo and Zhao Zhixiao and Hu Die and Wu Mengcheng and Lin Litao and Shen Si and Li Bin and Liu Jiangfeng and Zhang Hai and Zhao Lianzheng},
  year   = {2023},
  eprint = {2304.07778},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.07778v1}
}

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