SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanities
This work addresses the need for improved ancient text processing in digital humanities, offering potential benefits for organizing ancient information and promoting Chinese culture, though it appears incremental as it builds on existing GPT-type models.
The authors tackled the problem of intelligent processing of ancient texts in digital humanities by proposing SikuGPT, a GPT model based on the Siku Quanshu corpus, which outperforms other GPT-type models in tasks like intralingual translation and text classification.
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.