Papers like "Curated Synthetic Data Doesn't Have to Collapse" [1] and "How to Synthesize Text Data without Model Collapse?" [2] demonstrate it's possible to do this.
Since OpenAI's Privacy Policy [3] explicitly allows for the use of deidentified data, it's possible they consider rewrites (maybe paired with a model used to identify explicit PII) to be deidentified. Whether OpenAI's legal team thinks rewriting in this way technically means they aren't training on your data isn't something I'm able to comment on.
Here's the relevant Privacy Policy statement:
We also aggregate or de-identify Personal Data so that it no longer identifies you and use this information for the purposes described above, such as to analyze the way our Services are being used, to improve and add features to them, and to conduct research. We will maintain and use de-identified information in de-identified form and not attempt to reidentify the information, unless required by law.
[1]: https://arxiv.org/abs/2605.07724
> Anthropic agrees that Customer (a) retains all rights to its Inputs, and (b) owns its Outputs. Anthropic disclaims any rights it receives to the Customer Content under these Terms. Subject to Customer’s compliance with these Terms, Anthropic hereby assigns to Customer its right, title and interest (if any) in and to Outputs. Anthropic may not train models on Customer Content from Services. “Inputs” means submissions to the Services by Customer or its Users and “Outputs” means responses generated by the Services to Inputs (Inputs and Outputs together are “Customer Content”).
This is the only commercial ToS clause about how they handle your data for subscription users. They only promise not to train the model on your exact input and exact output. There's nothing about not washing your data - the "clean-room" approach, which is obviously easily automatable by a company that specializes in automation. That is not training a model on your data, it is using a model to create derivatives of your data, then training it on "their" derivatives.
People really needs to apply pressure and start demanding answers from these companies regarding this - because it is a huge problem. Historically, the amount of labor required to do something like this would make it entirely unfeasible, so this is all new territory. The existing laws and the requirements around clarity surrounding these conditions do not reflect the technology progress.
Could you please list a set of core papers (or other resources) that give a beginner an overview or even understand of the fundamental concepts and techniques with LLMs?
Thank you!
So I'd say, if you're motivated you could do the same. That said, I've always been a self-starter and I started my PhD after working for a decade. I'm sure there are other resources out there, but I'm not equipped to say what's best for a beginner (I found the original paper to be excellent, but most everyone during my PhD, including my advisor, found it to be inscrutable; I think it's written more like an engineering focused paper, which might be why researchers found it difficult to grok, but with my previous industry experience it seemed quite clear).