How Can Generative Artificial Intelligence Techniques Facilitate Intelligent Research into Ancient Books?

Generative artificial intelligence changes the paradigm of natural language processing research, sets off a new trend of research in computational humanities and computational social sciences, and provides unique perspectives on digital intelligence-enabled ancient book revitalization and intelligent applications. The article explores the role of multimodal large models in image processing and OCR of ancient books. I am discussing and exemplifying how to use Large Language Models for intelligent information processing of ancient texts. Explore combining prompt engineering, retrieval-enhanced generation (RAG), supervised fine-tuning, LangChain, and other techniques to improve performance in ancient text mining and applications. It also looks forward to the broad prospect of intelligent agent technology combined with the Large Language Model in the innovative application of ancient book revitalization. The research focuses on digitizing ancient books, intelligent processing of ancient texts, and intelligent application of ancient book revitalization. It demonstrates the feasibility, advancement, and creativity of the application of generative artificial intelligence and its derivative technologies in the field of computational humanities, especially in the field of ancient book preservation, to provide intelligent solutions for the dissemination of traditional thought and culture, from the perspective of the whole process of the technology of digital humanities and computational humanities research. The article also gives examples of the intelligent application of AI in the restoration of ancient books and the annotation of ancient texts. Although large language models demonstrate transformative potential in advancing the field of ancient text research toward intelligent analysis, there remain certain limitations. This article points out their shortcomings in areas such as knowledge completion for ancient texts, understanding emotions and cultural nuances, as well as ethical and accountability issues. It emphasizes the need for a more balanced perspective on the role that generative artificial intelligence plays in the exploration and utilization of cultural heritage.