The ChatGPT app injects history and all sorts of stuff into context automatically, I’ve seen it do it, and that might include something (like the user’s previous chats) that unintentionally give a style for the model to adopt.
To be specific, I saw ChatGPT reference a specific appointment of a family member in a context that was absent of that information, on a topic that had nothing to do with it. The only way it could have possibly known that was injecting the previous chat into context, silently.
That’s all I’m saying.
LLM architecture has nothing to do with that; its being done outside the LLM. By “pure”, I meant a chat interface where one can see the full prompt as input.
What I’m saying is, we don’t know if that’s the cause or not until we see the context. But it could plausibly be unintentional on the user’s part.
I promise I was not being “hostile”. I led with “I have literally no idea what you’re talking about”, and that’s because I frankly did not. I appreciate you clarifying, although I think dividing it into “pure” and (consequently) “impure” isn’t sensible terminology. An LLM is just that: a model trained on a huge text corpus.
I still get what you’re going for now, namely that output can be “tainted” by previous input, hence “purity”. I think something like “isolated input” would be more apt, although that’s also ad hoc, and there’s probably real, established terminology out there somewhere (that maybe I’m even forgetting).
there’s probably real, established terminology out there somewhere (that maybe I’m even forgetting).
Maybe… there may not be one, though. The ChatGPT app quite purposely obscures the mechanics of the LLM underneath, and they aren’t keen on advertising that to users.
“Isolated input” makes sense to me.
In a technical sense, I suppose an adjacent term would be a raw completion API? Which is rarely offered anymore, sadly.
Whoa. What’s with the jump to being so hostile?
The ChatGPT app injects history and all sorts of stuff into context automatically, I’ve seen it do it, and that might include something (like the user’s previous chats) that unintentionally give a style for the model to adopt.
To be specific, I saw ChatGPT reference a specific appointment of a family member in a context that was absent of that information, on a topic that had nothing to do with it. The only way it could have possibly known that was injecting the previous chat into context, silently.
That’s all I’m saying.
LLM architecture has nothing to do with that; its being done outside the LLM. By “pure”, I meant a chat interface where one can see the full prompt as input.
What I’m saying is, we don’t know if that’s the cause or not until we see the context. But it could plausibly be unintentional on the user’s part.
I promise I was not being “hostile”. I led with “I have literally no idea what you’re talking about”, and that’s because I frankly did not. I appreciate you clarifying, although I think dividing it into “pure” and (consequently) “impure” isn’t sensible terminology. An LLM is just that: a model trained on a huge text corpus.
I still get what you’re going for now, namely that output can be “tainted” by previous input, hence “purity”. I think something like “isolated input” would be more apt, although that’s also ad hoc, and there’s probably real, established terminology out there somewhere (that maybe I’m even forgetting).
Maybe… there may not be one, though. The ChatGPT app quite purposely obscures the mechanics of the LLM underneath, and they aren’t keen on advertising that to users.
“Isolated input” makes sense to me.
In a technical sense, I suppose an adjacent term would be a raw completion API? Which is rarely offered anymore, sadly.