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CODEIPPROMPT:对编码语言模型的知识产权侵权评估

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  • 1Washington University in St. Louis.

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此摘要是机器生成的。

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.

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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.