Honestly, thanks for writing all that out. I think I understand where you are coming from. You’re basically saying that this is an attribution issue and the LLMs cant determine that.

But at the same time, now you arent able to use properly attributed software either. Any open-source project that allows any user to contribute has a chance to be using unattributed code. Upstream code that your operating system uses to function could also be using unattributed code that your code runs through and could output tainted results.

The genie is out of the lamp, and it has already made the wishes of the user come true and that wish has landed in your repository. I just don’t see how its feasible to expect any open-source codebase to be fully compliant with full attribution. It never was to begin with, whether LLMs were used or not, but it was expected for people to abide by it.

Now that its easier to generate code off of the training data that went into the model, you have to be real selective on what code is used.

My suggestion for open-source projects is to recommend an open-weight model that has had its training data thoroughly audited so its not a black box. Have it cite code responsibly. Maybe only allow pull requests from users who use that particular model if they must use LLMs. That doesnt mean users are going to abide by that recommendation, but we should start guiding people towards a solution. People are going to use LLMs, thats not going away.