2 ms·

In ML, "best" is what minimises the loss function, so in a certain (very basic) sense it does.

• eru 1mo ago
  You are mixing up levels. When you are asking it a question (like which of these two texts is the best), the output is also just picked by minimising that loss function. There's no guarantee that answering "Text B is better" aligns with text B minimising the loss function. (And they aren't really minimising loss functions during inference. They sample from a distribution. During training they minimise the loss function of the distribution.)
  • greggoB 1mo ago
    Point taken, I was possibly overly curt in my response, leading to it being ambiguous, both re when the loss function is being minimised and that it is a stochastic process. So to OPs question: I guess LLMs do have an "idea" of what is best (conditioned on minimising a loss function during training), however they may not always output that (because stochasticity), which maybe represents a degree of uncertainty in that "idea"?
    • eru 1mo ago
      My point is that when you asked them to 'judge' two texts, they are just going to output the tokens that have the highest probability (or a sample from that distribution). But depending on their training data, these tokens might say 'correct horse battery staple', and not necessarily 'text A is better' or 'text B is better'. Even if text A would have been more likely to be produced by the LLM.