Wooldridge sees positives in the kind of AI depicted in the early years of Star Trek. In one 1968 episode, The Day of the Dove, Mr Spock quizzes the Enterprise’s computer only to be told in a distinctly non-human voice that it has insufficient data to answer. “That’s not what we get. We get an overconfident AI that says: yes, here’s the answer,” he said. “Maybe we need AIs to talk to us in the voice of the Star Trek computer. You would never believe it was a human being.”

Hmm. That's probably a pretty straightforward modification for existing LLMs, at least at the token level.

You can obtain token probabilities, so you can give some estimate out-of-band confidence in a response, down to the token level. Don't really need to change anything for that, just expose some data.

And you could make the AI aware of its own neural net's confidence level, feed the confidence back into the neural net for subsequent tokens, see if you can get it to take that information into account.

https://en.wikipedia.org/wiki/Recurrent_neural_network

In artificial neural networks, recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series,[1] where the order of elements is important. Unlike feedforward neural networks, which process inputs independently, RNNs utilize recurrent connections, where the output of a neuron at one time step is fed back as input to the network at the next time step. This enables RNNs to capture temporal dependencies and patterns within sequences.