CODEIPPROMPT:对编码语言模型的知识产权侵权评估
Zhiyuan Yu1, Yuhao Wu1, Ning Zhang1
1Washington University in St. Louis.
概括
代码生成的大型语言模型 (LMs) 经常因为训练数据而侵犯知识产权. 我们的平台CODEIPPROMPT通过人工智能生成的代码来评估和突出这些知识产权风险.
科学领域:
- 人工智能
- 软件工程
- 知识产权法
背景情况:
- 大型语言模型 (LMs) 在合成编程代码方面表现出先进的能力.
- 人工智能生成的代码的兴起引发了对知识产权 (IP) 侵犯的重大担忧.
- 在生成代码的LM中探索知识产权问题仍然是一个相对未经探索的领域.
研究的目的:
- 引入CODEIPPROMPT,这是一个用于自动评估LM产生的代码中侵犯知识产权的新平台.
- 评估LM复制许可程序的程度,并确定潜在的知识产权侵权行为.
- 调查违反知识产权的根本原因,并探索缓解策略.
主要方法:
- 开发CODEIPPROMPT,使用来自授权代码数据库的提示来触发侵犯知识产权的代码生成.
- 在CODEIPPROMPT中实施测量工具,以量化LM生成代码中的IP违规程度.
- 使用CODEIPPROMPT平台对各种开源和商业代码LM进行了广泛的评估.
主要成果:
- 在所有评估的开源和商业代码LM中观察到侵犯知识产权的普遍性.
- 主要原因是训练数据集中包含限制性许可代码.
- 意图包含和不一致的现实许可实践都导致了这一问题.
结论:
- 对于评估当前代码生成平台中的知识产权侵权风险, CODEIPPROMPT 是一个关键的测试平台.
- 该研究强调迫切需要加强减缓策略,以解决AI代码合成中的知识产权问题.
- 微调和动态令牌过是减少知识产权侵权的潜在方法.
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