ICML2023
CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models
Zhiyuan Yu, Yuhao Wu, Ning Zhang, Chenguang Wang, Yevgeniy Vorobeychik, Chaowei Xiao
42 citations
Abstract
Recent advances in large language models (LMs) have facilitated their ability to synthesize programming code. However, they have also raised concerns about intellectual property (IP) rights violations. Despite the significance of this issue, it has been relatively less explored. In this paper, we aim to bridge the gap by presenting CODEIPPROMPT 1 , a platform for automatic evaluation of the extent to which code language models may reproduce licensed programs. It comprises two key components: prompts constructed from a licensed code database to elicit LMs to generate IP-violating code, and a measurement tool to evaluate the extent of IP violation of code LMs. We conducted an extensive evaluation of existing open-source code LMs and commercial products and revealed the prevalence of IP violations in all these models. Our study provides a testbed for evaluating the IP violation issues of code generation platforms and stresses the need for a better mitigation strategy.