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It’s not naive; tesseract does this.

• rafram 2y ago
  Tesseract doesn’t use an LLM. LLMs don’t know how confident they are; Tesseract’s model does.
  • touisteur 2y ago
    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 2y ago
    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 2y ago
      True, but LLM token probability doesn't map nearly as cleanly to "how readable was the text".
      • hansvm 2y ago
        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)?