I think it might be a mistake to think that the model is not confident because its response is something a human might say if they were not confident. The model is 'just' completing the prefix text with something that has high likelihood from its perspective, so it may just be used to, for instance, seeing people hedge in similar conversations it has read in its training data.
More generally, whether these models are well-calibrated (that is, they know what they don't know) is an important area of research. I don't have references offhand, but I think it's true broadly speaking that these larger pre-trained models do tend to be better calibrated.
More generally, whether these models are well-calibrated (that is, they know what they don't know) is an important area of research. I don't have references offhand, but I think it's true broadly speaking that these larger pre-trained models do tend to be better calibrated.