Leveraging generative AI through prompt engineering for corpus construction and in-depth intelligent interpretation of ancient texts.
High-quality domain corpora constitute the cornerstone of computational humanities research. This study investigates the foundational methodologies and optimization strategies that leverage large language models (LLMs) in conjunction with prompt engineering to facilitate the construction and in-dept...
| Publicado en: | Digital Scholarship in the Humanities Vol. 40; no. 3; pp. 846 - 863 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Sep2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=188027894&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 188027894 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Sep2025 vid: 40 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 188027894 10.1093/llc/fqaf043 ppf: 846 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P size: 2.1MB tig: atl: Leveraging generative AI through prompt engineering for corpus construction and in-depth intelligent interpretation of ancient texts. aug: au: Liu, Jiangfeng Yang, Chunhua Yan, Zhaoping Ma, Xueliang Pei, Lei affil: School of Information Management, Nanjing University, Nanjing 210023, China Laboratory of Data Intelligence and Cross Innovation, Nanjing University, Nanjing 210023, China School of Health Economic and Management, Nanjing University of Chinese Medicine, Nanjing 210023, China Department of Publication, National Library of China, Beijing 100081, China su: Generative artificial intelligence Digital humanities Corpora Ancient literature Natural language processing Interdisciplinary research sug: subj: Generative artificial intelligence Digital humanities Corpora Ancient literature Natural language processing Interdisciplinary research keyword: AIGC ChatGPT computational humanities & digital humanities generative artificial intelligence prompt engineering ab: High-quality domain corpora constitute the cornerstone of computational humanities research. This study investigates the foundational methodologies and optimization strategies that leverage large language models (LLMs) in conjunction with prompt engineering to facilitate the construction and in-depth analysis of classical text corpora. The research systematically categorizes prevalent prompt frameworks and presents a comprehensive prompt optimization protocol, delineating common chain-of-thought enhancement approaches. It examines the application of prompt engineering-driven LLMs to address conventional natural language processing tasks for ancient texts. The study demonstrates the viability of prompt engineering methodologies based on large models for the intelligent processing and application of classical texts, offering relevant Artificial Intelligence (AI) digital humanities product exemplars as efficacious references for developing high-caliber corpora in the computational humanities domain. Future investigations should contemplate further exploration of AI technology's potential in humanities scholarship, fostering interdisciplinary collaboration across humanities and social sciences disciplines, including history, literature, philosophy, computer science, and information science. Ongoing attention should be directed toward the technological impact on promoting and preserving exemplary traditional Chinese culture, ensuring judicious technological implementation alongside the safeguarding of traditional cultural knowledge and values. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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