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Titlebook: Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration; 25th International C Dion H. Goh,Shu-Jiun Che

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發(fā)表于 2025-3-21 17:35:34 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration
副標(biāo)題25th International C
編輯Dion H. Goh,Shu-Jiun Chen,Suppawong Tuarob
視頻videohttp://file.papertrans.cn/586/585405/585405.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration; 25th International C Dion H. Goh,Shu-Jiun Che
描述.This two-volume set LNCS 14457 and LNCS 14458 constitutes the refereed proceedings of the 25th International Conference on Asia-Pacific Digital Libraries, ICADL 2023, held in Taipei, Taiwan, during December 4-7, 2023. .The 15 full, 17 short, 2 practice papers and 12 poster papers presented in this volume were carefully reviewed and selected from 85 submissions.. .Based on significant contributions, the full and short papers have been classified into the following topics: include information retrieval, knowledge extraction and discovery, cultural and scholarly data, information seeking and use, digital archives and data management, design and evaluation of information environments, and applications of GAI in digital libraries..
出版日期Conference proceedings 2023
關(guān)鍵詞Cultural Data Analysis; Data Management; Digital Archives; Digital Libraries; Generative Artificial Inte
版次1
doihttps://doi.org/10.1007/978-981-99-8085-7
isbn_softcover978-981-99-8084-0
isbn_ebook978-981-99-8085-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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Evaluating the?Use of?Generative LLMs for?Intralingual Diachronic Translation of?Middle-Polish Textsperformance of the trained models was far from perfect, the quality of translations produced with ChatGPT was good in most cases. Although caution should be exercised, we believe that LLMs have a high potential for text-to-text annotation applications.
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