Generative large-scale language models create the fifth paradigm of
scientific research, organically combine data science and computational
intelligence, transform the research paradigm of natural language processing
and multimodal information processing, promote the new trend of AI-enabled
social science research, and provide new ideas for digital humanities research
and application. This article profoundly explores the application of
large-scale language models in digital humanities research, revealing their
significant potential in ancient book protection, intelligent processing, and
academic innovation. The article first outlines the importance of ancient book
resources and the necessity of digital preservation, followed by a detailed
introduction to developing large-scale language models, such as ChatGPT, and
their applications in document management, content understanding, and
cross-cultural research. Through specific cases, the article demonstrates how
AI can assist in the organization, classification, and content generation of
ancient books. Then, it explores the prospects of AI applications in artistic
innovation and cultural heritage preservation. Finally, the article explores
the challenges and opportunities in the interaction of technology, information,
and society in the digital humanities triggered by AI technologies.

大规模语言模型在数字人文研究中的应用深入探讨，揭示了它们在古籍保护、智能处理和学术创新方面的重要潜力，通过具体案例展示了人工智能在古籍组织、分类和内容生成中的辅助作用，并探索了在艺术创新和文化遗产保护中的应用前景，最后探讨了由人工智能技术引发的数字人文领域中技术、信息和社会相互交融的挑战和机遇。

从 ChatGPT、DALL-E 3 到 Sora：生成型人工智能如何改变数字人文研究与服务？

From ChatGPT, DALL-E 3 to Sora: How has Generative AI Changed Digital  Humanities Research and Services?

Combining computational technologies and humanities is an ongoing effort
aimed at making resources such as texts, images, audio, video, and other
artifacts digitally available, searchable, and analyzable. In recent years,
deep neural networks (DNN) dominate the field of automatic text analysis and
natural language processing (NLP), in some cases presenting a super-human
performance. DNNs are the state-of-the-art machine learning algorithms solving
many NLP tasks that are relevant for Digital Humanities (DH) research, such as
spell checking, language detection, entity extraction, author detection,
question answering, and other tasks. These supervised algorithms learn patterns
from a large number of "right" and "wrong" examples and apply them to new
examples. However, using DNNs for analyzing the text resources in DH research
presents two main challenges: (un)availability of training data and a need for
domain adaptation. This paper explores these challenges by analyzing multiple
use-cases of DH studies in recent literature and their possible solutions and
lays out a practical decision model for DH experts for when and how to choose
the appropriate deep learning approaches for their research. Moreover, in this
paper, we aim to raise awareness of the benefits of utilizing deep learning
models in the DH community.

将计算技术与人文学科相结合是一项不断努力的工作，旨在使文本、图像、音频、视频和其他人工制品等资源能够以数字化形式进行存储、检索和分析。本文分析最近文献中多种人文学科研究的使用案例，探讨了人文学科研究中采用深度学习方法面临的训练数据和领域适应性方面的挑战，并提出了实用的决策模型，以指导人文学科专家在何时以及如何选择合适的深度学习方法进行研究。此外，本文旨在提高人文学科社区对于利用深度学习模型的收益的认知。

数字人文与信息科学中的深度神经网络文本分析

Text Analysis Using Deep Neural Networks in Digital Humanities and  Information Science

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.

本研究提出了一种名为 SikuGPT 的 GPT 模型，旨在处理古代文本并超过其他 GPT 型模型的性能，有望促进中国古代文化的国际传播和古代信息的组织。