[ttyprintk]

It’s not naive; tesseract does this.

[rafram]

Tesseract doesn’t use an LLM. LLMs don’t know how confident they are; Tesseract’s model does.

[touisteur]

With most Machine Learning algorithms I used to get shapley values or other 'explainable AI' metrics (for a large cost compared to simple inference, yes), it's very unsettling and frustrating to work without them now on LLMs.

[hansvm]

Kind of. Tesseract's confidence is just a raw model probability output. You could easily use the entropy associated with each token coming out of an LLM to do the same thing.

[rafram]

True, but LLM token probability doesn't map nearly as cleanly to "how readable was the text".

[hansvm]

Why not though? Both kinds of models jumble around the data and spit out a probability distribution. Why is the tesseract distribution inherently more explainable (aside from the UI/UX problem of the uncertainty being per-token instead of per-character)?