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| 題名: | 大型語言模型權重之著作權議題
Copyright-Related Legal Considerations for Large Language Model Weights |
| 作者: | 張瑀舫
Chang, Yu-Fang |
| 貢獻者: | 宋皇志
王文杰
Sung, Huang-Chih
Wang, Wen-Chieh
張瑀舫
Chang, Yu-Fang |
| 關鍵詞: | 生成式人工智慧
大型語言模型
權重
著作權適格性
衍生著作
Generative Artificial Intelligence
Large Language Models
Model Weights
Copyright Eligibility
Derivative Works |
| 日期: | 2025 |
| 上傳時間: | 2025-09-01 16:08:37 (UTC+8) |
| 摘要: | 2022年起，大型語言模型（LLM）的快速發展徹底改變了內容生成的典範，卻也對既有的著作權體系構成前所未有的挑戰。從訓練資料的合法取得到生成內容的權利歸屬，現行法律框架顯得捉襟見肘，引發了學術界與實務界的廣泛討論。
多數討論僅聚焦於大型語言模型訓練端與輸出端之著作權議題，對於大型語言模型內部重要成分——權重——則甚少著墨。然而，大型語言模型之權重係決定模型表現之關鍵因素，近兩年來隨著開源之推動，權重之可得性提高，市場競爭格局因而產生變化，權重是否得受著作權保護將成為法律與科技雙重領域的關鍵議題。
本研究旨在探討大型語言模型權重之著作權適格性議題。研究核心問題為：權重是否符合著作權之各項要件？權重屬於何種著作類型？權重是否為訓練資料之衍生著作？微調後權重是否為微調前權重之衍生著作？
透過跨學科文獻歸納及比較法分析，本研究發現，權重應屬固著於一定有形媒介之原創性表達，應受著作權保護，屬於美國法下之文學著作；我國法下則未必構成語文著作，而屬法規未例示之類型。權重與訓練資料間不具實質相似性，並非對訓練資料之改作，然微調後權重則構成微調前權重之衍生著作。
隨著越來越多科技公司加入開源權重之陣營，第三人可輕易取得該些權重並自行微調以適配特定任務，權重獨立受著作權保護之必要性愈發彰明較著。期能藉由本研究之探討，豐富大型語言模型權重於著作權領域之討論，為相關領域帶來新的思考方向與討論契機。
Since 2022, the rapid advancement of large language models (LLMs) has fundamentally altered the paradigm of content Generation, while simultaneously presenting unprecedented challenges to existing copyright regimes. Issues such as the lawful acquisition of training data and the attribution of rights in generated outputs have exposed the limitations of current legal frameworks, thereby sparking significant academic and practical discourse.
To date, the majority of discussions have concentrated on copyright issues pertaining to the training and output stages of LLMs. In contrast, relatively little attention has been paid to a core internal element of such models—their weights. Yet model weights are a determinative factor in the performance of LLMs. In recent years, the growing trend toward open-sourcing has rendered weights increasingly accessible, thereby reshaping the competitive landscape. Against this backdrop, the question of whether model weights are eligible for copyright protection has emerged as a critical issue at the intersection of intellectual property law and emerging technologies.
This article seeks to examine the copyright eligibility of LLM weights. The central questions addressed include: whether model weights satisfy the statutory requirements for copyright protection; the appropriate classification of weights within existing categories of copyrightable subject matter; whether model weights constitute derivative works of the underlying training data; and whether fine-tuned weights may be regarded as derivative works of the pre-fine-tuned weights.
Employing an interdisciplinary methodology that combines doctrinal legal analysis with comparative law perspectives, this study finds that model weights constitute original expressions fixed in a tangible medium of expression, and as such, should be eligible for copyright protection. Under U.S. copyright law, they may be classified as literary works; under Taiwanese law, however, they may not fall within the statutory definition of “linguistic works,” and instead may constitute an unenumerated category of protected works. Furthermore, there is no substantial similarity between the model weights and the training data; accordingly, weights should not be deemed derivative works of such data. By contrast, fine-tuned weights may satisfy the criteria for derivative works in relation to the original model weights.
As more technology companies adopt open-source licensing practices with respect to model weights, enabling third parties to easily obtain and adapt these weights for task-specific purposes, the need for independent copyright protection of model weights becomes increasingly apparent. It is hoped that this study will contribute to the growing body of literature concerning the intersection of artificial intelligence and copyright law, and provide a foundation for future legal inquiry and policy development in this area. |
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（四）司法判決
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26、Steeplechase Arts & Prods., L.L.C. v. Wisdom Paths, Inc., 652 F. Supp. 3d 481 (D.N.J. 2023).
27、Tandy Corp. v. Pers. Micro Computers, Inc., 524 F. Supp. 171 (N.D. Cal. 1981)
28、Victor Lalli Enters., Inc. v. Big Red Apple, Inc., 936 F.2d 671 (2d Cir. 1991).
（五）政府資料
1、Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS)
2、Berne Convention(1979 Paris Act).
3、H.R. REP. 94-1476, 51, 1976 U.S.C.C.A.N. 5659.
4、U.S. Copyright Office, “Cancellation Decision re: Zarya of the Dawn (VAu001480196), ” (Feb. 21, 2023), https://www.copyright.gov/docs/zarya-of-the-dawn.pdf. (last visited: 2025.07.01)
5、WIPO Copyright Treaty – WCT.
6、World Intellectual Property Organization. WIPO Intellectual Property Handbook: Policy, Law and Use(WIPO Publication, 2004), 441. https://tind.wipo.int/record/28661/files/wipo_pub_489.pdf.
（六）網際網路
1、“The Open Source Definition,” open source initiative, July 7, 2006 (last modified: February 16, 2024). https://opensource.org/osd. (last visited: 2025.07.01)
2、AIVA. “AIVA,” n.d. https://creators.aiva.ai/. (last visited: 2025.07.01)
3、Akshit Mehra. “Data Collection and Preprocessing for Large Language Models.” LABELLERR, September 27, 2024. https://www.labellerr.com/blog/data-collection-and-preprocessing-for-large-language-models/. (last visited: 2025.07.01)
4、baidu. “ERNIE-4.5-300B-A47B-Paddle.” Hugging Face, June 28, 2025. https://huggingface.co/baidu/ERNIE-4.5-300B-A47B-Paddle. (last visited: 2025.07.01)
5、Chat GPT Is Eating the World. “Updated Map of All 42 Copyright Suits v. AI Companies (Jun. 12, 2025),” June 12, 2025. https://chatgptiseatingtheworld.com/2025/06/12/updated-map-of-all-42-copyright-suits-v-ai-companies-jun-12-2025/. (last visited: 2025.07.01)
6、CourtListener. “Kadrey v. Meta Platforms, Inc. Complaint — Document #1,” January 25, 2024. https://www.courtlistener.com/docket/67569326/1/kadrey-v-meta-platforms-inc/.(last visited: 2025.07.01)
7、CourtListener. Zhang v. Google LLC Complaint — Document #1,” April 26, 2024. https://www.courtlistener.com/docket/68477933/1/zhang-v-google-llc/.(last visited: 2025.07.01)
8、deepseek-ai. “DeepSeek-R1.” Hugging Face, n.d. https://huggingface.co/deepseek-ai/DeepSeek-R1/tree/main. (last visited: 2025.07.01)
9、derek-thomas. “Datasets: Derek-Thomas/ScienceQA.” Hugging Face, February 10, 2023. https://huggingface.co/datasets/derek-thomas/ScienceQA/viewer?views%5B%5D=train&row=18. (last visited: 2025.07.01)
10、GitHub. “DeepSeek-V3: Open-Source Weights and Inference Code,” Last modified December 2024. https://github.com/deepseek-ai/DeepSeek-V3. (last visited: 2025.07.01)
11、Has the AI rally gone too far, UBS, https://www.ubs.com/global/en/wealthmanagement/insights/chief-investment-office/house-view/daily/2023/latest-25052023.html. (last visited: 2025.07.01)
12、Huzefa Chawre. “Understanding Data Processing Techniques for LLMs.” TURING, November 21, 2023. https://www.turing.com/resources/understanding-data-processing-techniques-for-llms. (last visited: 2025.07.01)
13、Imarena-ai. “Chatbot Arena LLM Leaderboard: Community-driven Evaluation for Best LLM and AI chatbots.” Hugging Face, n.d. https://huggingface.co/spaces/lmarena-ai/chatbot-arena-leaderboard. (last visited: 2025.07.01)
14、Ivan Belcic, and Cole Stryker. “What Are Model Parameters?” IBM, May 5, 2025. https://www.ibm.com/think/topics/model-parameters. (last visited: 2025.07.01)
15、Jason Gong. “Is DeepSeek Open Source? Why It Matters in 2025.” bardeen, February 10, 2025. https://www.bardeen.ai/answers/is-deepseek-open-source. (last visited: 2025.07.11)
16、Leonard Lin. “Shisa V2 405B: Japan’s Highest Performing LLM.” SHISA. AI, June 3, 2025. https://shisa.ai/posts/shisa-v2-405b/. (last visited: 2025.07.01)
17、meta-llama. “Meta-Llama-3-8B.” Hugging Face, n.d. https://huggingface.co/meta-llama/Meta-Llama-3-8B/tree/main. (last visited: 2025.07.01)
18、Noor Fatima. “Understanding the Key Files in a Large Language Model (LLM).” Medium, November 29, 2024. https://medium.com/@noorfatimaafzalbutt/understanding-the-key-files-in-a-large-language-model-llm-bce17a027370. (last visited: 2025.07.01)
19、Open Source Seed Initiative. “About,” n.d. https://osseeds.org/about/. (last visited: 2025.07.01)
20、Perplexity AI Team. “Open-Sourcing R1 1776.” Perplexity AI, February 19, 2025. https://www.perplexity.ai/hub/blog/open-sourcing-r1-1776. (last visited: 2025.07.01)
21、Qwen. “Qwen-7B-Chat.” Hugging Face, September 24, 2023. https://huggingface.co/Qwen/Qwen-7B-Chat. (last visited: 2025.06.18)
22、Rick Merritt. “AI Opener: OpenAI’s Sutskever in Conversation With Jensen Huang.” NVIDIA, March 22, 2023. https://blogs.nvidia.com/blog/sutskever-openai-gtc/. (last visited: 2025.07.01)
23、Robert Hulse, Tyler G. Newby, Stuart P. Meyer, and Fredrick Tsang. “DeepSeek, Model Distillation, and the Future of AI IP Protection.” FENWICK, February 3, 2025. https://www.fenwick.com/insights/publications/deepseek-model-distillation-and-the-future-of-ai-ip-protection. (last visited: 2025.07.01)
24、sanjayMSFT. “Where Does an LLM Keep All That Knowledge? A Peek into the Physical Side of AI.” Microsoft Tech Community, May 5, 2025. https://techcommunity.microsoft.com/blog/machinelearningblog/where-does-an-llm-keep-all-that-knowledge-a-peek-into-the-physical-side-of-ai/4410287. (last visited: 2025.07.01)
25、Stable Diffusion. “Stable Diffusion Online,” n.d. https://stablediffusionweb.com/. (last visited: 2025.07.01)
26、wikimedia. “Datasets: wikimedia/wikipedia.” Hugging Face, Nov 10, 2023. https://huggingface.co/datasets/wikimedia/wikipedia/viewer/20231101.zh?views%5B%5D=_20231101zh. (last visited: 2025.07.01) |
| 描述: | 碩士
國立政治大學
科技管理與智慧財產研究所
112364210 |
| 資料來源: | http://thesis.lib.nccu.edu.tw/record/#G0112364210 |
| 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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