[6gvONxR4sf7o]

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 still works otherwise, but is less concise to explain). When it then autocompletes, it samples from that distribution (a weighted coin flip, in your terms).

So it’s just neat that the weights in the coin flip don’t match what is asked for.

[intended]

Just to check we are on the same page -

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?