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LiCoEval: Evaluating LLMs on License Compliance in Code Generation

arxiv.org · 11,275 words · saved by 1 readers

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. Recent advances in Large Language Models (LLMs) have revolutionized code generation, leading to widespread adoption of AI coding tools by developers. However, LLMs can generate license-protected code without providing the necessary license information, leading to potential intellectual property violations during software production. This paper addresses the critical, yet underexplored, issue of license compliance in LLM-generated code by establishing a benchmark to evaluate the ability of LLMs to provide accurate license information for their generated code. To establish this benchmark, we conduct an empirical study to identify a reasonable standard for “striking similarity” that excludes the possibility of independent creation, indicating a copy relatio

LiCoEval: Evaluating LLMs on License Compliance in Code Generation Weiwei Xu1, Kai Gao4, Hao He2, Minghui Zhou13 3Minghui Zhou is the corresponding author. 1 School of Computer Science, Peking University, Beijing, China 1 Key Laboratory of High Confidence Software Technologies, Ministry of Education, China 4 University of Science and Technology Beijing, Beijing, China 2 Carnegie Mellon University, Pittsburgh, USA xuww@stu.pku.edu.cn, kai.gao@ustb.edu.cn, haohe@andrew.cmu.edu, zhmh@pku.edu.cn Abstract Recent advances in Large Language Models (LLMs) have revolutionized code generation, leading t

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