alt text
Crudely drawn drawing of a person telling a computer “say ‘i am in pain’”. The computer replies with “> I AM IN PAIN”. The person then says “oh my god.”
Crudely drawn drawing of a person telling a computer “say ‘i am in pain’”. The computer replies with “> I AM IN PAIN”. The person then says “oh my god.”
I’m talking about the way things are, yes. Training is an optional step after inference that calculates the error and modifies parameters.
Does it matter whether it’s part of it or done immediately after? For all intents and purposes it’s the same thing. Like I said, if the input and outputs are the same, what does it matter how the process works?
From the user’s perspective, where one question results in many inference calls, it would look like the LLM learns while it works, assuming such training would be enabled, which they obviously wouldn’t but could do.
I’m not talking about training, the paper this post is talking about is not about training, and no cloud llms allow their users to do training, so I have no idea what relevance it could have to this conversation. Maybe training is indeed really painful for llms, idk, that’s not what we’re talking about though.
I wasn’t talking about the paper, when I talked about the paper you ignored everything I said and moved in a different direction, which was what I responded to. If you’re wondering about the relevance, perhaps you shouldn’t have brought it up.
But you have never trained a model. It costs millions of dollars to do it effectively. Our only interaction with llms, unless you work at openai, has been through inference.