大模型蒸馏技术应用的合法性证成

Justification of Legitimacy for the Application of Large Model Distillation Technology

中文关键词:  人工智能  大语言模型  模型蒸馏  不正当竞争  技术滥用  数据公开

英文关键词:artificial intelligence  large language models  model distillation  unfair competition  technological abuse  data disclosure

中文摘要:

      大模型蒸馏技术的应用引起了人工智能领域关于技术创新与剽窃的激烈争议。此项技术是具有中立性的模型训练方法,其技术价值在于能够缓解大语言模型训练数据短缺、算力资源紧张的问题,降低模型训练成本且提升训练效率,对人工智能产业可持续发展具有积极价值。当技术应用以大语言模型研发为根本目的时,应秉持鼓励创新的基本立场,通过合理的制度调适既肯定技术应用的合法性,又能防范技术滥用风险。一方面,应用技术从付费使用的“教师模型”中提取数据并用于“学生模型”训练,不应被视为违反大语言模型用户协议中有关使用限制的约定,亦不属于不正当竞争行为；另一方面,技术应用理当在善意且合理的范围内进行,不得侵犯“教师模型”运营者的合法权益。恶意妨碍“教师模型”的正常运行、未经同意擅自公开蒸馏所得数据集合等,对此技术应用者仍需承担法律责任。 

英文摘要:

      The application of the large model distillation technology has triggered intense controversy in the field of artificial intelligence (AI) regarding the boundary between technical “innovation” and “plagiarism”. As a neutral model training method, this technology delivers significant value by alleviating the shortages of training data and computing resources for large language models (LLMs), reducing model training costs, and improving training efficiency, thus advancing the sustained development of the AI industry. When the applications of this technology are fundamentally aimed at LLM research and development, a stance that encourages innovation should be upheld. In this context, reasonable institutional interpretation serves to affirm the legitimacy of such applications and prevent the risks of technological abuse. On the one hand, applying this technology to extracting data from a paid-access teacher model to train a student model should not be deemed a violation of the usage restrictions specified in the LLM user agreements, nor should it be classified as an act of unfair competition. On the other hand, this technology must be applied in good faith and within reasonable bounds, without infringing upon the legitimate rights and interests of the teacher model's operator. The technology adopter shall bear legal liability for actions such as maliciously interfering with the normal operation of a teacher model or disclosing distilled datasets without authorization. 