Sure, you can fix that in a couple lines. Then a couple more lines for evaluating multiple questions on the same answer in parallel. Then a couple more lines for the confidence score (which is trivial to compute from all we have, but missing regardless). Then a harness to fine-tune an existing model to perform better on this specific task, and a collection of training data to use for that

I think we can all agree that Jev is not rocket science. It's a good idea executed well, with marketing that might have been a tad too bold

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porridgeraisin 7 hours ago | parent [-]

The confidence score is not trivial to compute. That is the whole point of the model. Even if you are using a proper scoring function such as NLL, it is not enough to ensure calibration in deep nets. So you have to do good post training to ensure it. These are all known techniques, but they are far from trivial, especially on large scale datasets.

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wongarsu 7 hours ago | parent [-]

Their docs at https://docs.typesafe.ai/confidence state "confidence is a statistic computed from the probability distribution the answer already gives you. TypeSafe computes it for you"

And further down "TypeSafe computes confidence from how the probability is spread across the options. All of it on one option gives 1.0; the more evenly it spreads, the lower the confidence. This demo uses (3 × largest probability − 1) / 2 to approximate confidence for three options."

So while we don't know the exact formula they use, it is just a function over the probabilities

I am open to the argument that this does not work well if you just plug in a qwen model instead of a model that is trained to output more statistically useful token distributions

	▲	kantahayashi 3 hours ago \| parent \| next [-]
		For N options, it's (N x Max Probability - 1) / (N - 1). It's verified in this article: https://bernoulli.app/articles/is-jev-confident It means confidence is just a converted max probability and not an independent signal.
	▲	porridgeraisin 7 hours ago \| parent \| prev [-]
		> I am open to the argument we agree then, that is the entirety of my argument. Getting a deep net especially one that is anywhere near even SLM size to be calibrated is tough, especially across domains. They claim calibration across a variety of datasets which is interesting.