[2304.07778] SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanities
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Computer Science > Computation and Language

arXiv:2304.07778 (cs)
[Submitted on 16 Apr 2023]

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

Authors:Liu Chang, Wang Dongbo, Zhao Zhixiao, Hu Die, Wu Mengcheng, Lin Litao, Shen Si, Li Bin, Liu Jiangfeng, Zhang Hai, Zhao Lianzheng
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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.
Comments: 20 pages,1 figure
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2304.07778 [cs.CL]
  (or arXiv:2304.07778v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2304.07778

Submission history

From: Liu Chang [view email]
[v1] Sun, 16 Apr 2023 13:25:24 UTC (393 KB)
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