Study on Risks and Countermeasures of Knowledge Distillation Technology for AI Large Models

Li Yunlin

Qingdao University of Science and Technology，Qingdao City, Shandong Province，266000;

Abstract:As a core technology in AI large model compression and transfer learning, knowledge distillation, with its efficient training logic of "teacher-student model", has become a standard practice in the research and development of various models. However, beneath its technical neutrality and efficiency, compliance dilemmas have emerged as a core issue restricting its application: in practice, disputes over the identification of infringement in "unauthorized distillation of others' proprietary models" have exposed the disjunction between existing legal regulations and technological development. Focusing on the core issue of "resolving the compliance dilemmas of knowledge distillation technology", this paper, by dismantling the trade secrets and copyright protection systems potentially involved in distillation behaviors, and integrating domestic and foreign legislation, industry cases, and technical characteristics, constructs a governance path of "technology tracing - rule clarification - institutional guarantee" with fair use as its foundation. This path not only avoids suppressing technological innovation due to excessive regulation but also prevents infringement risks arising from regulatory gaps, thereby providing a scheme for the standardized development of the large model industry.

Keywords: Knowledge Distillation; Large Model Training; Fair Use

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