Comment by adastra22
1 year ago
My beef is that “You can help improve Claude” doesn’t properly convey that in doing so you are effectively making your chats public / globally accessible.
1 year ago
My beef is that “You can help improve Claude” doesn’t properly convey that in doing so you are effectively making your chats public / globally accessible.
You're likely conflating the public/shared chats bug with "we'll use your data to train" case (the latter is what's dicussed here)
No, I am not. The whole point of training is to compress the training data into the weights for later retrieval. It is lossy compression, but not by as much as you might think. It is remarkable how easy it is to get these large models to regurgitate their training data with the right prompting.
What? You are not "effectively making your chats globally accessible".
There is no situation in which I could access your chats. If you disagree, kindly explain how I do that.
> There is no situation in which I could access your chats. If you disagree, kindly explain how I do that
You are dead wrong here. Let me explain.
Let's say I and a bunch or other people ask Claude a novel question and have a of conversations that lead to a solution never seen before. Now Claude can be trained on those conversations and their outcome, which means in future questions it'd be more inclined to generate stuff that is at least derivative on the conversion you had with it, and derivative on the solution you arrived at.
Which is exactly what the OP hints at.
> Let's say I and a bunch or other people ask Claude a novel question
Not that ‘novel’ then, is it?
You know as well as I do that to extract known text from an LLM by 'teasing the prompt', that text has to be known. See: the NYT's lawsuit. [0]
So if you don't know the text of my 'novel question', how do you suggest extracting it?
[0]: https://kagi.com/search?q=nyt+lawsuit+openai&r=au&sh=-NNFTwM...
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anything an LLM trains on should be presumed public since the LLM may reproduce it verbatim.