{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T13:09:57Z","timestamp":1780319397055,"version":"3.54.1"},"reference-count":22,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T00:00:00Z","timestamp":1733529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Construction and Application of Pre-Trained Models for Traditional Chinese Medicine Classics","award":["2023SJZD084"],"award-info":[{"award-number":["2023SJZD084"]}]},{"name":"Transforming Literature Knowledge Organisation and Evaluation Research with Generative Artificial Intelligence","award":["KYCX24_0111"],"award-info":[{"award-number":["KYCX24_0111"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Comput. Cult. Herit."],"published-print":{"date-parts":[[2024,12,31]]},"abstract":"<jats:p>Generative AI 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. We discuss and exemplify how to use Large Language Models for intelligent information processing of ancient texts and explore combining prompt engineering, retrieval augmented generation (RAG), supervised fine-tuning, LangChain, and other techniques to improve performance in ancient text mining and applications. This article 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 AI 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 AI plays in the exploration and utilization of cultural heritage.<\/jats:p>","DOI":"10.1145\/3690391","type":"journal-article","created":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T14:55:18Z","timestamp":1727967318000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["How Can Generative Artificial Intelligence Techniques Facilitate Intelligent Research into Ancient Books?"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7268-7313","authenticated-orcid":false,"given":"Jiangfeng","family":"Liu","sequence":"first","affiliation":[{"name":"School of Information Management, Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1238-2582","authenticated-orcid":false,"given":"Xueliang","family":"Ma","sequence":"additional","affiliation":[{"name":"National Library of China, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3984-8343","authenticated-orcid":false,"given":"Lanyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Chinese Language and Literature, Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4754-4112","authenticated-orcid":false,"given":"Lei","family":"Pei","sequence":"additional","affiliation":[{"name":"School of Information Management, Nanjing University, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,12,7]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.13998\/j.cnki.issn1002-1248.22-0359"},{"key":"e_1_3_1_3_2","unstructured":"General Office of the CPC Central Committee General Office of the State Council. The General Office of the Central Committee of the Communist Party of China and the General Office of the State Council issued the Opinions on Promoting the Work on Ancient Books in the New Era. Retrieved 22 January 2024 from https:\/\/www.gov.cn\/zhengce\/2022-04\/11\/content_5684555.htm"},{"key":"e_1_3_1_4_2","first-page":"21","article-title":"Analysis of the Legal and Policy Basis of the Legislation on Protection of Ancient Books","volume":"2","author":"Peng Yi","year":"2022","unstructured":"Yi Peng. 2022. Analysis of the Legal and Policy Basis of the Legislation on Protection of Ancient Books. The Library Journal of Shandong 2 (2022), 21\u201324.","journal-title":"The Library Journal of Shandong"},{"issue":"4","key":"e_1_3_1_5_2","first-page":"100","article-title":"On the Relationship between the Publication and the Protection of Ancient Books","volume":"38","author":"Gu Lei","year":"2020","unstructured":"Lei Gu. On the Relationship between the Publication and the Protection of Ancient Books. Journal of Academic Libraries 38, 4 (2020), 100\u2013105. DOI: https:\/\/doi.org\/10\/gtfcwt","journal-title":"Journal of Academic Libraries"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.13530\/j.cnki.jlis.2022013"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.19764\/j.cnki.tsgjs.20190489"},{"key":"e_1_3_1_8_2","unstructured":"Gautier Izacard Patrick Lewis Maria Lomeli Lucas Hosseini Fabio Petroni Timo Schick Jane Dwivedi-Yu Armand Joulin Sebastian Riedel and Edouard Grave. 2022. Atlas: Few-Shot Learning with Retrieval Augmented Language Models. Retrieved 22 January 2024 from http:\/\/arxiv.org\/abs\/2208.03299"},{"key":"e_1_3_1_9_2","unstructured":"Zhiheng Xi Wenxiang Chen Xin Guo Wei He Yiwen Ding Boyang Hong Ming Zhang Junzhe Wang Senjie Jin Enyu Zhou Rui Zheng Xiaoran Fan Xiao Wang Limao Xiong Yuhao Zhou Weiran Wang Changhao Jiang Yicheng Zou Xiangyang Liu Zhangyue Yin Shihan Dou Rongxiang Weng Wensen Cheng Qi Zhang Wenjuan Qin Yongyan Zheng Xipeng Qiu Xuanjing Huang and Tao Gui. 2023. The Rise and Potential of Large Language Model Based Agents: A Survey. Retrieved 22 January 2024 from http:\/\/arxiv.org\/abs\/2309.07864"},{"key":"e_1_3_1_10_2","first-page":"95","article-title":"The OCR Technology of Ancient Books in the Intelligent Age","volume":"3","author":"Wang Jun","year":"2022","unstructured":"Jun Wang, Chenglin Liu, Lianwen Jin, Yongge Liu, Chiyi Zhang, Yinfei Wang, Hui Zhu, Jingwen Han, and Xuan Xu. 2022. The OCR Technology of Ancient Books in the Intelligent Age. Digital Humanities 3 (2022), 95\u2013125.","journal-title":"Digital Humanities"},{"issue":"10","key":"e_1_3_1_11_2","first-page":"125","article-title":"Historical Chinese Seal Text Recognition Based on ResNet and Transfer Learning","volume":"58","author":"Chen Yaya","year":"2022","unstructured":"Yaya Chen, Quanxiang Liu, Kaili Wang, and Yaohua Yi. 2022. Historical Chinese Seal Text Recognition Based on ResNet and Transfer Learning. Computer Engineering and Applications 58, 10 (2022), 125\u2013131.","journal-title":"Computer Engineering and Applications"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2023.06.013"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10032-022-00401-y"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/app10165430"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.47294\/KSBDA.23.4.27"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1117\/12.2585629"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1088\/1757-899X\/225\/1\/012149"},{"key":"e_1_3_1_18_2","unstructured":"Alec Radford Jong Wook Kim Chris Hallacy Aditya Ramesh Gabriel Goh Sandhini Agarwal Girish Sastry Amanda Askell Pamela Mishkin Jack Clark Gretchen Krueger and Ilya Sutskever. 2021. Learning Transferable Visual Models from Natural Language Supervision. Retrieved 27 January 2024 from http:\/\/arxiv.org\/abs\/2103.00020"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","unstructured":"Peng Xu Xiatian Zhu and David A. Clifton. 2023. Multimodal Learning with Transformers: A Survey. arXiv:2206.06488. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2206.06488","DOI":"10.48550\/arXiv.2206.06488"},{"key":"e_1_3_1_20_2","first-page":"6","article-title":"A New Approach for Information Resources Management Empowered by Data Intelligence: The Concept, Connotation and Development of Instruction Engineering","volume":"1","author":"Lu Wei","year":"2024","unstructured":"Wei Lu, Lei Wang, Qikai Cheng, Jianwei Liu, and Yong Huang. 2024. A New Approach for Information Resources Management Empowered by Data Intelligence: The Concept, Connotation and Development of Instruction Engineering. Documentation, Information & Knowledge 1 (2024), 6\u201311.","journal-title":"Documentation, Information & Knowledge"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","unstructured":"Yizhong Wang Yeganeh Kordi Swaroop Mishra Alisa Liu Noah A. Smith Daniel Khashabi and Hannaneh Hajishirzi. 2023. Self-Instruct: Aligning Language Models with Self-Generated Instructions. arXiv:2212.10560. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2212.10560","DOI":"10.48550\/arXiv.2212.10560"},{"key":"e_1_3_1_22_2","unstructured":"Leonie Monigatti. 2023. Retrieval-Augmented Generation (RAG): From Theory to LangChain Implementation. Medium. Retrieved 11 February 2024 from https:\/\/towardsdatascience.com\/retrieval-augmented-generation-rag-from-theory-to-langchain-implementation-4e9bd5f6a4f2"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","unstructured":"Yunfan Gao Yun Xiong Xinyu Gao Kangxiang Jia Jinliu Pan Yuxi Bi Yi Dai Jiawei Sun Qianyu Guo Meng Wang and Haofen Wang. 2024. Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv:2312.10997. Retrieved from https:\/\/doi.org\/10.48550\/arXiv.2312.10997","DOI":"10.48550\/arXiv.2312.10997"}],"container-title":["Journal on Computing and Cultural Heritage"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690391","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690391","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:09:36Z","timestamp":1750295376000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690391"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,7]]},"references-count":22,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,12,31]]}},"alternative-id":["10.1145\/3690391"],"URL":"https:\/\/doi.org\/10.1145\/3690391","relation":{},"ISSN":["1556-4673","1556-4711"],"issn-type":[{"value":"1556-4673","type":"print"},{"value":"1556-4711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,7]]},"assertion":[{"value":"2024-05-10","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-08-21","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}