Earlier quoted context omitted.
I think I maybe get where you are coming from, but still how? I feel we are discussing 2 different use cases. 1) Prompt 1: “ You are a weighted random choice generator. About 80% of the time please say ‘left’ and about 20% of the time say ‘right’. Simply reply with left or right. Do not say anything else" ” 2) Assume that the training data gives examples of 2.1) single coin flips 2.2) multiple coin flips Consider a s…
Nope, i’m describing prompt 1. The output of the model is a distribution over tokens, which is then sampled by the system to get the next word/token. This is what people mean when they talk about the logits in these models. So if you ask it for one single sample, you hope it might give you a logit vector that corresponds to 80% ‘left’ token and 20% ‘right’ token (assuming those words are single tokens, but it all sti…
The output is the probability that x is the correct n+1 token based on the input of n tokens.
You are stating that the output will be a probability distribution where token n+1 has a chance to be 80% left and 20% right.
In essence, when the model evaluates the input, at some level it comprehends the semantics of the input and then does a weighted coin flip.
What I am stating is that based on the given input prompt, the nature of an LLM and the training data the output will be "Left"
The LLM will not be doing a coin flip at this stage, since it’s prediction is only text based.
The input vector constrains it to 80% left. Since it’s training data is human text, this essentially constrains the first output token to left 100% of the time.
If you try to have it provide tokens n+2,n+3… etc in the same output, then it will start spitting out right.
Are these the two positions at play here? Have I represented you correctly, and have I represented myself accurately?