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Journal of library and information science in agriculture ›› 2025, Vol. 37 ›› Issue (2): 37-48.doi: 10.13998/j.cnki.issn1002-1248.25-0139

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Risks and Regulations for Application of the LLaMA Model in University Future Learning Centers

QIAO Jinhua1, MA Xueyun2()   

  1. 1. School of Law, Shandong University, Qingdao 266237
    2. Shandong University Library, Qingdao 266237
  • Received:2025-01-03 Online:2025-02-05 Published:2025-05-20
  • Contact: MA Xueyun

Abstract:

[Purpose/Significance] The rapid advancement of artificial intelligence (AI) technology is transforming various sectors, particularly in higher education. The LLaMA (Large Language Model Meta AI) represents a significant innovation in this arena, making its application within university future learning centers increasingly important. As institutions of higher education strive to create environments conducive to learning and growth, understanding the construction requirements of future learning centers becomes paramount. This study delves into the integration of LLaMA core technologies in these learning spaces and emphasizes the importance of evolving libraries into intelligent learning support systems. [Method/Process] The methodology employed in this research combines technical deconstruction and scenes for validation, allowing for a comprehensive analysis of the legal risks associated with embedding advanced technologies in educational frameworks. By systematically examining these potential risks, the study aims to establish a well-rounded perspective on the implications of AI deployment in educational settings. [Results/Conclusions] The study identifies three principal challenges encountered in the application of the LLaMA within university learning centers. The first challenge arises from reliability risks linked to content generated by the AI, which may be affected by biases present in the training data. Such biases can lead to the dissemination of inaccurate or misleading information, undermining the trustworthiness of educational resources. Secondly, there are privacy leakage risks, particularly associated with the retention of user behavioral data. As AI systems analyze user interactions, there is a potential for sensitive information to be exposed or misused, raising concerns about student privacy and data security. The third challenge involves ownership determination dilemmas regarding the content generated through AI-driven creative processes. These dilemmas are intricately tied to existing copyright law frameworks, which may not adequately address the complexities introduced by human-machine collaboration in content creation. In response to these challenges, the study proposes several pathways for governance aimed at effectively navigating the landscape of AI in education. It suggests the implementation of dynamic data cleansing mechanisms to address reliability risks and inaccuracies. Additionally, establishing tiered privacy protection systems can help safeguard against user data breaches. Legal frameworks also need refinement to ensure clear ownership distribution for outputs of human-machine collaboration. Ultimately, optimizing the application of the LLaMA model in university future learning centers necessitates a careful balance between technological innovation and legal regulation. By focusing on technical refinement, risk control, and relevant regulatory measures, the development and application of AI can be advanced, facilitating a more integrated evolution of artificial intelligence and educational practices.

Key words: future learning centers, university libraries, LLaMA, generative artificial intelligence, AIGC, LLM

CLC Number: 

  • G250.7

Table 1

Comparation between LLaMA and ChatGPT"

区别 ChatGPT LLaMA
训练方法 对话数据微调 大规模预训练
逻辑推理表现 一般 较好
模型大小 1 750亿参数 8亿、13亿、70亿参数
数据来源 训练数据由OpenAI精心挑选和整理,来源广泛,包括互联网文本等,在特定领域知识更深入准确 LLaMA 3使用来自公开来源的15.6万亿个token进行训练
主要优势 强大的语言理解和生成能力、多领域广泛适用、交互体验好、推理能力较强 开源与可制定性高、参数效率高、多语言处理能力强、特定领域表现出色
主要劣势 闭源限制、存在信息准确性问题、计算资源和成本要求高 知识储备相对有限、对话连贯性有待提高、算法复杂度较低

Fig.1

Application of LLaMA large model in university future learning centers"

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