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

Hu Die; Li Bin; Lin Litao; Liu Chang; Liu Jiangfeng; Shen Si; Wang Dongbo; Wu Mengcheng; Zhang Hai; Zhao Lianzheng

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

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arxiv 2304.07778 v1 pith:FJB5ASAO submitted 2023-04-16 cs.CL

Liu Chang , Wang Dongbo , Zhao Zhixiao , Hu Die , Wu Mengcheng , Lin Litao , Shen Si , Li Bin

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Liu Jiangfeng Zhang Hai Zhao Lianzheng

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classification cs.CL

keywords ancienttextsdigitalhumanitiesmodelprocessingresearchsikugpt

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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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