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| 題名: | AI大型語言模型著作權問題之研究 ——以大型語言模型訓練資料為中心
Research on Copyright Issues of AI Large Language Models: Focusing on Training Data for Large Language Models |
| 作者: | 邱思齊
Chiou, Sz-Chi |
| 貢獻者: | 馮震宇
Fong, Jerry G.
邱思齊
Chiou, Sz-Chi |
| 關鍵詞: | 生成式AI
大型語言模型
訓練資料侵權問題
合理使用
著作權
Generative AI
Large Language Models
Training Data Copyright Infringement
Fair Use
Copyright |
| 日期: | 2024 |
| 上傳時間: | 2025-09-01 16:05:44 (UTC+8) |
| 摘要: | AI大型語言模型著作權問題於Chat GPT開啟之生成式AI時代中，一直是備受關注的議題，尤其自2023年紐約時報公司對Open AI和微軟公司提起訴訟後，大型語言訓練資料著作權侵權問題亦成為實務和學說討論的重心。故本文以「大型語言模型使用受著作權保護的資料進行AI訓練是否構成侵權」作為核心問題，以案例探討為主，文獻回顧為輔展開討論。
本文首先探討大型語言模型之訓練流程、區辨文本和資料探勘技術僅為大型語言模型訓練階段之輔助技術，並揭示GPT-3訓練資料集中Common Crawl、WebText2和Book2相關著作權爭議；接著，將美國、歐盟、法國、的國、英國、日本、新加坡、中國和韓國對於AI訓練資料之政策或規範，分成沿用合理使用原則、明確設立TDM例外規定與態度尚未明確三類比較分析，並接續討論近期以Open AI為被告之大型語言模型文字訓練資料訴訟，統整各案件原告對被告之侵權指控，主要包括被告模型訓練階段侵犯重製權，與輸出階段重製權、改作權、公開展示權與散布權。最後，討論合理使用原則、TDM例外規定與臺灣現行法制，於以Open AI為被告之大型語言模型文字訓練資料訴訟之適用，除針對核心問題提出本文見解，也對臺灣於AI訓練資料之法制建構提出建議。
The issue of copyright in large language models (LLMs) has become a highly debated topic in the era of generative AI initiated by tools like ChatGPT. Particularly after the New York Times filed lawsuits against OpenAI and Microsoft in 2023, the focus of both practical and academic discussions has shifted to the copyright infringement of training data used for large language models. This paper addresses the core question: "Does the use of copyrighted materials in AI training for large language models constitute infringement?" It employs a case-based analysis supplemented by a review of relevant literature.
First, this paper examines the training process of LLMs, highlighting that text and data mining technologies serve merely as auxiliary techniques in the training stage. It also reveals copyright controversies surrounding datasets used to train GPT-3, such as Common Crawl, WebText2, and Book2. Subsequently, it conducts a comparative analysis of policies and regulations related to AI training data across jurisdictions, including the United States, the European Union, France, Germany, the United Kingdom, Japan, Singapore, China, and South Korea. These are categorized into three approaches: reliance on the fair use principle, explicit establishment of text and data mining (TDM) exceptions, and unclear regulatory attitudes.
The paper further discusses recent lawsuits involving OpenAI as a defendant over the use of textual training data for LLMs, summarizing plaintiffs' infringement allegations, which primarily include violations of reproduction rights during the training phase, as well as reproduction, adaptation, public display, and distribution rights during the output phase. Finally, the paper explores the applicability of the fair use principle, TDM exceptions, and Taiwan’s current legal framework to such lawsuits. It provides insights into the core question and offers recommendations for Taiwan’s legal framework on AI training data. |
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22.Marita Skjuve, Petter Bae Brandtzaeg, and Asbjørn Følstad, Why do people use ChatGPT? Exploring user motivations for generative conversational AI, First Monday, volume 29(1), 7-9 (2024). Available at: https://dx.doi.org/10.5210/fm.v29i1.13541
23.Matthew Sag, Copyright and Copy-Reliant Technology., Northwestern University Law Review, Vol.103. 1607, 1644-1673 (2009). Available at SSRN: https://ssrn.com/abstract=1257086
24.Matthew Sag, Copyright Safety for Generative AI, Houston Law Review, Vol. 61, No. 2. 295, 295-296(2023). Available at SSRN: https://ssrn.com/abstract=4438593
25.Matthew Sag and Peter K. Yu, The Globalization of Copyright Exceptions for AI Training, Emory Law Journal, Vol. 74, 2025, Forthcoming, Texas A&M University School of Law Legal Studies Research Paper Forthcoming, Emory Legal Studies Research Paper Forthcoming. 1, 16-22(2024). Available at SSRN: https://ssrn.com/abstract=4976393
26.Matthew Sag, The New Legal Landscape for Text Mining and Machine Learning, Journal of the Copyright Society of the USA, Vol. 66. 291, 293 (2019). Available at: https://ssrn.com/abstract=3331606
27.Michael W. Carroll, Copyright and the Progress of Science: Why Text and Data Mining Is Lawful, 53 UC Davis Law Review 893, 2019, American University, WCL Research Paper No. 2020-15. 893, 959 (2019). Available at SSRN: https://ssrn.com/abstract=3531231
28.Nestor Maslej, Loredana Fattorini, Raymond Perrault, Vanessa Parli, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald and Jack Clark, The AI Index 2024 Annual Report, AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, 47(2024). Available at: https://arxiv.org/abs/2405.19522
29.Pierre N. Leval, in Toward a Fair Use Standard, 103 HARV. L. REV. 1105, 1111 (1990). Available at: https://doi.org/10.2307/1341457.
30.Rossana Ducato and Alain Strowel, Limitations to Text and Data Mining and Consumer Empowerment: Making the Case for a Right to Machine Legibility, CRIDES Working Paper Series. 1, 15 (2018). Available at SSRN: https://ssrn.com/abstract=327890
31.Shahid Iqbal Rai, Maida Maqsood, Bushra Hanif, Muhammad Ali Adam, Muhammad Arslan, Hira Shafiq and Muhammad Sijawal, Computational linguistics at the crossroads: A comprehensive review of NLP advancements, World Journal of Advanced Engineering Technology and Sciences, 11(02).578, 579-581(2024). Available at: https://wjaets.com/sites/default/files/WJAETS-2024-0146.pdf
32.Shervin Minaee, et al., Large Language Models: A Survey, arXiv:2402.06196, v2,1(2024). Available at: https://arxiv.org/abs/2402.06196
33.Sobel, Benjamin, A Taxonomy of Training Data Disentangling the Mismatched Rights, Remedies and Rationales for Restricting Machine Learning, In Jyh-An Lee, Reto M Hilty, and Kung-Chung Liu (ed.), Artificial Intelligence and Intellectual Property 221, 229 (2021). Available at: https://academic.oup.com/book/39560/chapter-abstract/339433034?redirectedFrom=fulltext
34.Stefan Baack, A Critical Analysis of the Largest Source for Generative AI Training Data: Common Craw, ACM, 5(2024). Available at https://doi.org/10.1145/3630106.3659033
35.Stefan Baack, Training Data for the Price of a Sandwich1 Common Crawl’s Impact on Generative AI, Mozilla Insights, 1-20(2024). Available at: https://foundation.mozilla.org/en/research/library/generative-ai-training-data/
36.Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever and Dario Amodei, Language Models Are Few-Shot Learners, arXiv:2005.14165, v4, 5-9(2020). Available at: https://arxiv.org/abs/2005.14165
37.Trieu H. Trinh, Quoc V. Le, A Simple Method for Commonsense Reasoning, arXiv:1906.02847, v2,12(2019). Available at: https://arxiv.org/abs/1806.02847
38.Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie and Ji-Rong Wen, A Survey of Large Language Models, arXiv: 2303.18223, v14,17(2024). Available at: https://arxiv.org/abs/2303.18223
39.Yang Liu, Jiahuan Cao, Chongyu Liu, Kai Ding and Lianwen Jin, Datasets for Large Language Models: A Comprehensive Survey, arXiv:2402.18041,84(2024). Available at: https://arxiv.org/abs/2402.18041
40.Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba and Sanja Fidler, Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books, arXiv:1506.06724, 3(2015). Available at: https://arxiv.org/abs/1506.06724
論文（韓文）
1.홍승기（洪昇基），데이터마이닝 면책 입법 방향에 대한 의문（關於資料探勘豁免立法方向的問題），경영법률(經營法律)，第32卷第4期，頁34，2022年。
2.차상육（車相祿），저작권법상 인공지능 학습용 데이터셋의 보호와 쟁점 ―텍스트・데이터마이닝(TDM) 면책규정을 중심으로―（著作權法下人工智慧可訓練資料集保護的法律問題 - 聚焦文字和資料探勘（TDM）的著作權例外 -）， 경영법률（經營法律）, 第32卷第１期，頁42，2021年10月。
官方報告
1.文化庁，著作権法の一部を改正する法律（平成30年法律第30号）について，載於：https://www.bunka.go.jp/seisaku/chosakuken/hokaisei/h30_hokaisei/（最後瀏覽日：2024年10月25日）。
2.柿沼太一，進化する機械学習パラダイス ～改正著作権法が日本のAI開発をさらに加速する～，STORIA法律事務所，載於：https://storialaw.jp/blog/4936 （最後瀏覽日：2024年10月25日）
3.류시원（柳時媛），인공지능 시대 저작권 정책 형성절차에 관한 제언（人工智慧時代著作權政策形成程序的建議），법제처（法制處），頁115-116，2024年。載於：https://www.moleg.go.kr/boardDownload.es?bid=mpblegpst&list_key=134243&seq=1
4.DSIT, Consultation outcome A pro-innovation approach to AI regulation: government response, GOV.UK, available at https://www.gov.uk/government/consultations/ai-regulation-a-pro-innovation-approach-policy-proposals/outcome/a-pro-innovation-approach-to-ai-regulation-government-response
5.DSIT, Policy paper A pro-innovation approach to AI regulation, GOV.UK, available at https://www.gov.uk/government/publications/ai-regulation-a-pro-innovation-approach/white-paper (last visited Oct. 31, 2024).
6.Intellectual Property Office Of Singapore, Factsheet on Copyright Act 2021, 14(2022), available at:https://www.ipos.gov.sg/docs/default-source/resources-library/copyright/copyright-act-factsheet.pdf
7.Japan Copyright Office, General Understanding on AI2 and Copyright in Japan: Overview, Agency for Cultural Affairs of Japan, 10(2024), available at https://www.bunka.go.jp/english/policy/copyright/pdf/94055801_01.pdf
8.JOSEPH R. BIDEN JR., Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence THE WHITE HOUSE, available at https://www.whitehouse.gov/briefing-room/presidential-actions/2023/10/30/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence/ (last visited Oct. 31, 2024).
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11.State of Israel Ministry of Justice, Opinion: Uses of Copyright Materials for Machine Learning, 40(2022), available at https://www.gov.il/BlobFolder/legalinfo/machine-learning/he/18-12-2022.pdf
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判決
1.Andy Warhol Found. for the Visual Arts v. Goldsmith, 598 U.S. 1(U.S., 2023).
2.Andy Warhol Found. for the Visual Arts, Inc. v. Goldsmith, 11 F.4th 26 (2d Cir. 2021).
3.Authors Guild v. Google, Inc., 804 F.3d 202 (2d Cir. 2015).
4.Authors Guild, Inc. v. HathiTrust, 755F.3d87,105(2dCir.2014)
5.A.V. exrel.Vanderhye v. iParadigms, LLC,562F.3d630,634(4thCir.2009).
6.Campbell v. Acuff-Rose Music, 510 U.S. 569 (1994).
7.Cariou, v. Prince, 714 F.3d 694 (2d Cir. 2013).
8.Google LLC v. Oracle Am., Inc., 141 S. Ct. 1183 (2021).
9.Harper & Row v. Nation Enterprises, 471 U.S. 539(1985)
10.Perfect10, Inc. v. Amazon. co, Inc., 508 F.3d 1146 (9thCir.2007).
裁定
1.Judge’s Order on Motion to Dismiss, The Intercept Media, Inc. v. OpenAI, Inc., 1:24-cv-01515 (S.D.N.Y. ruled on 21 Nov. 2024)
2.Judge’s Memorandum & Opinion, The New York Times Company v. Microsoft Corporation, 1:23-cv-11195 (S.D.N.Y. ruled on 22 Nov. 2024)
3.Judge’s Order on Motion to Dismiss, Raw Story Media, Inc. v. OpenAI Inc., 1:24-cv-01514 (S.D.N.Y. ruled on 7 Nov. 2024)
4.Order Modified by Judge, Authors Guild v. OpenAI Inc., 1:23-cv-08292 (S.D.N.Y. filed on 16 Feb. 2024).
5.Order on Motion to Dismiss, Tremblay v. OpenAI, Inc., 3:23-cv-03223 (N.D. Cal. ruled on 12 ＦFeb. 2024)
6.Order on Stipulation, 4, Tremblay v. OpenAI, Inc., 3:23-cv-03223, Silverman v. OpenAI, Inc., 3:23-cv-03416, Chabon v. OpenAI, Inc., 3:23-cv-04625(N.D. Cal. filed on 12 Feb. 2024)
卷證
1.Anthropic ’s Motion for Preliminary Injunction, Concord Music Group, Inc. v. Anthropic PBC, 5:24-cv-03811(N.D. Cal. filed on 6 Jan. 2024)
2.Anthropic ’s Motion to Dismiss, Concord Music Group, Inc. v. Anthropic PBC, 5:24-cv-03811(N.D. Cal. filed on 15 Aug. 2024)
3.Microsoft’s Motion to Dismiss, The New York Times Company v. Microsoft Corporation, 1:23-cv-11195(S.D.N.Y. filed on 4 Mar. 2024)
4.Microsoft’s Memorandum of Law in Support of Motion, The New York Times Company v. Microsoft Corporation, 1:23-cv-11195(S.D.N.Y. filed on 4 Mar. 2024)
5.OpenAI’s Answer to Complaint, Authors Guild v. OpenAI Inc., 1:23-cv-08292; Alter v. OpenAI Inc., 1:23-cv-10211, (S.D.N.Y. filed on 4 Mar. 2024).
6.Open AI’s Memorandum of Law in Support of Motion, Tremblay v. OpenAI, Inc., 3:23-cv-03223 (N.D. Cal. filed on 28 Aug. 2023)
7.Open AI’s Motion to Dismiss, The New York Times Company v. Microsoft Corporation, 1:23-cv-11195, (S.D.N.Y. filed on 26 Feb. 2024)
8.Open AI’s Memorandum of Law in Support of Motion, The New York Times Company v. Microsoft Corporation, 1:23-cv-11195, (S.D.N.Y. filed on 26 Feb. 2024)
9.Open AI’s Motion to Dismiss, Tremblay v. OpenAI, Inc., 3:23-cv-03223 (N.D. Cal. filed on 28 Aug. 2023)
10.Plaintiff’s Complaint (first consolidated amended), Authors Guild v. OpenAI Inc., 1:23-cv-08292(S.D.N.Y. filed on 2 May 2024)
11.Plaintiff’s Complaint, Basbanes v. Microsoft Corporation, 1:24-cv-00084, (S.D.N.Y. filed on 5 Jan. 2024)
12.Plaintiff’s Complaint (first amended), Chabon v. OpenAI, Inc., 3:23-cv-04625, (N.D. Cal. filed on 5 Oct. 2023)
13.Plaintiff’s Complaint, Concord Music Group, Inc. v. Anthropic PBC, 5:24-cv-03811, (N.D. Cal. filed on 8 Oct. 2023)
14.Plaintiff’s Complaint, Daily News LP v. Microsoft Corporation, 1:24-cv-03285, (S.D.N.Y. filed on 3 Apr. 2024)
15.Plaintiff’s Complaint, The Center for Investigative Reporting, Inc. v. OpenAI, Inc., 1:24-cv-04872, (S.D.N.Y. filed on 24 Sep. 2024)
16.Plaintiff’s Complaint, The New York Times Company v. Microsoft Corporation, 1:23-cv-11195 (S.D.N.Y. filed on 27 Dec. 2023)
17.Plaintiff’s Complaint (first consolidated amended), Tremblay v. OpenAI, Inc., 3:23-cv-03223, Silverman v. OpenAI, Inc., 3:23-cv-03416, Chabon v. OpenAI, Inc., 3:23-cv-04625(N.D. Cal. filed on 13 Mar. 2024)
18.Re: Updated Joint Dispute Chart, Authors Guild v. OpenAI Inc., 1:23-cv-08292; Alter v. OpenAI Inc., 1:23-cv-10211, (S.D.N.Y. filed on 22 Nov. 2024).
19.Re: Updated Joint Dispute Chart, The New York Times Company v. Microsoft Corporation, 1:23-cv-11195, (S.D.N.Y. filed on 22 Nov. 2024)
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| 描述: | 碩士
國立政治大學
科技管理與智慧財產研究所
111364204 |
| 資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0111364204 |
| 資料類型: | thesis |
| 顯示於類別: | [科技管理與智慧財產研究所] 學位論文
|
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描述 |
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在政大典藏中所有的資料項目都受到原著作權保護.
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