"Slop is its own cure"

My hypothesis for Jev is that they simply generate many answers independently in parallel from your prompt, and then discard the duplicates (or train to avoid duplication with attention between the ). In that way the entire batch is 1 user's prompt.

Option 1 is not. In part because it is opt-out (will it turn back on on its own like my Facebook privacy settings?), and not always respected (sending feedback can mean your chat is used?). Also because disabling "Improve model for everyone" is very vague.

There simply needs to be a setting like "my data is confidential", in which case there clear guarantees like there are for ZDR.

As an example, I've seen people speculate that while input prompts and output tokens are discarded, thinking traces are retained for training, which could leak information. I doubt this is true, but it shows that the policy is not unambiguous and reassuring enough to remove all doubt.

Thanks for asking.

You can have a look at the literature on exact copying in image models if it interests you, but just online we often see online examples of agents outputting code that already exists, even if its not the common case.

I very much doubt OpenAI points the model towards a specific conversation, but these trillion parameter models can very much "remember" their training data. For instance I can ask GPT to summarize my papers from their title alone, without looking them up, and it works decently.

There is a difference between claiming an action is a transgression, and claiming it could be one.

In answer to a post suggesting that training on a datapoint could mean plagiarism, you said that this would imply that all outputs are plagiarized. This is not the case, no, because generative models do not "copy" or "create", they do both at different times.

I did not agree or disagree with the original poster, I was explaining to you why I thought you disagreed with them. If you understand what I said above, then why do you disagree with them?

EDIT: I just saw your other post on "general inspiration" and I believe I read the situation exactly; you appear to believe that inputs used to train generative models get "lost in the parameter soup", but it is not always the case.

All this to say, trust is important, and grounded in social convention. So I do agree with you, but also disagree.

Whenever this is OK or not really depends on how the breakthrough is contextualized, and how there people at play, here, agree to contextualize it.

In my view, in the blog post, there is much discussion about who will be publishing the paper. If instead it was just a blog post that said "oops, we beat you to it, our model is the best", it would have been different.

I remained impressed by ChatGPT however!