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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.”
What I said applies to all machine learning models ever created: inference does not make any impact on a model. Any question you ask it slides off of it just like, apparently, any attempt to explain things to you.
Have you done any ML? Because if you had, you’d know that statement is complete bullshit.
Of course. What I said is like the simplest concept to understand about machine learning, the difference between training and inference.
Then you know that there is nothing stopping you from training during inference, or to modify weights in reponse to inference. It just so happens to be more efficient to train the next model instead, but again that goes back to what I said, there is no need to copy humans in this because humans aren’t efficient in everything, and certainly not in training.
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.