It’s all code, the people coding it are 100% capable of programming it to keep track of where the information comes from. Even if it’s transformative, that doesn’t prevent it from keeping track of what was transformed.

This is a fundamental misunderstanding of how LLMs actually work. Given a list of previous tokens, a complicated set of linear algebra and normalization operations are applied to yield the “probability” (in quotes because this is a dubious application of the word imo) that each known token will follow it. The model is trained using an equally complicated regression algorithm that slowly adjusts the billions of linear algebra coefficients to more closely match the training data. RLHF is then used to make more adjustments that allow the AI to fulfill its intended purpose (e.g., to reinforce the question-answer format expected of ChatGPT).

You may recall regression from your first statistics class. Even in the case of simple linear regression, when the input consists of millions of data points, it is essentially impossible to determine which point should be “credited” for any given aspect of the output line. The same is true for AI: you could maybe compile a list of training data that makes a token “likely” to appear after another token, but nothing more complex than that. It is very rare for a small set of sources to be responsible for a sequence longer than a few tokens.

I do, however, believe they should be required to provided a very specific list of sources used for training the model. I think it’s ridiculous to claim that generative AI is transformative in a practical sense: I can’t imagine it would be legal for companies to make endless photocopies of copyrighted material and have a computer make fancy scrapbooks out of it, even if “it’s a fledgling industry” or whatever.