> The problem with LLMs is that the output is inherently stochastic - i.e there isn't a "I don't have enough information" option. This is due to the fact that LLMs are basically just giant look up maps with interpolation.
I don't think this explanation is correct. The input to the decoder at the end of all the attention heads etc (as I understand it) is a probability distribution over tokens. So the model as a whole does have an ability to score low confidence in something by assigning it a low probability.
The problem is that thing is a token (part of a word). So the LLM can say "I don't have enough information" to decide on the next part of a word but has no ability to say "I don't know what on earth I'm talking about" (in general - not associated with a particular token).
I don't think this explanation is correct. The input to the decoder at the end of all the attention heads etc (as I understand it) is a probability distribution over tokens. So the model as a whole does have an ability to score low confidence in something by assigning it a low probability.
The problem is that thing is a token (part of a word). So the LLM can say "I don't have enough information" to decide on the next part of a word but has no ability to say "I don't know what on earth I'm talking about" (in general - not associated with a particular token).