> Do any LLMs do this? If not, why can't they? If they could, then the user would just need to pick a threshold that suits their peace of mind and review any outputs that came back below that threshold.
That's not how they work - they don't have internal models where they are sort of confident that this is a good answer. They have internal models where they are sort of confident that these tokens look like they were human generated in that order. So they can be very confident and still wrong. Knowing that confidence level (log p) would not help you assess.
There are probabilistic models where they try to model a posterior distribution for the output - but that has to be trained in, with labelled samples. It's not clear how to do that for LLMs at the kind of scale that they require and affordably.
You could consider letting it run code or try out things in simulations and use those as samples for further tuning, but at the moment, this might still lead them to forget something else or just make some other arbitrary and dumb mistake that they didn't make before the fine tuning.
That's not how they work - they don't have internal models where they are sort of confident that this is a good answer. They have internal models where they are sort of confident that these tokens look like they were human generated in that order. So they can be very confident and still wrong. Knowing that confidence level (log p) would not help you assess.
There are probabilistic models where they try to model a posterior distribution for the output - but that has to be trained in, with labelled samples. It's not clear how to do that for LLMs at the kind of scale that they require and affordably.
You could consider letting it run code or try out things in simulations and use those as samples for further tuning, but at the moment, this might still lead them to forget something else or just make some other arbitrary and dumb mistake that they didn't make before the fine tuning.