[sumtechguy]

LLMs and their close buddies NN's use models that do massive amounts of what amounts to cubic splines across N dimensions.

Cubic splines have the same issues as what these nets are seeing. There are two points and a 'line of truth' between them. But the formula that connects the dots, as it were, only guarantees that the two points are inside the line. You can however tweak the curve to line fit but it is not always 100%, in fact can vary quite wildly. That is the 'hallucination' people are seeing.

Now can you get that line of truth close by more training? Which is basically amounts to tweaking the weighting. Usually yes, but the method basically only guarantees the points are inside the line. Everything else? Well, it may or may not be close. Smear that across thousands of nodes and the error rate can add up quickly.

If we want a confidence level my gut is saying that we would need to measure how far away from the inputs an output ended up being. The issue that would create though is the inputs are now massive. Sampling can make the problem more tractable but then that has more error in it. Another possibility is tracking how far away from the 100% points the output gave. Then a crude summation might be a good place to start.