The (ab)use of Open Source Code to Train Large Language Models
This work addresses legal and ethical dilemmas for developers and organizations using LLMs trained on copyleft code, but it is incremental as it builds on known memorization concerns without introducing new methods.
The paper tackles the problem of large language models (LLMs) for code memorizing and emitting verbatim source code from unsanitized training datasets, highlighting security, privacy, and licensing risks, and provides four actionable recommendations to address these issues.
In recent years, Large Language Models (LLMs) have gained significant popularity due to their ability to generate human-like text and their potential applications in various fields, such as Software Engineering. LLMs for Code are commonly trained on large unsanitized corpora of source code scraped from the Internet. The content of these datasets is memorized and emitted by the models, often in a verbatim manner. In this work, we will discuss the security, privacy, and licensing implications of memorization. We argue why the use of copyleft code to train LLMs is a legal and ethical dilemma. Finally, we provide four actionable recommendations to address this issue.