About jev being a black box and the potential for bias, I think it boils down to what questions you are asking the model.
Broad questions like Is this resume good / score this city will ofcourse be biased but I think jev encourages more granular focused questions like Score this candidates Python experience / Rate this city for its food which then allows you to introduce your own biases in which questions you ask and how you combine their answers.
In this way I think jev like models can be easier to reason about for critical decisions.
I think "Black boxes are back in fashion" is missing the point. I think LLMs are still largely black boxes, and I don't think chain of thought is representative of any degree of inner machination. Asking it questions to justify itself is at best a facsimile, and for the most part it's useful, but it's fundamentally a facsimile.
Where I understand Jev to be a significant jump is that afaik the confidence scoring is actually derived from the normalised probabilities, and not a continuation in a chain of prediction masquerading as "confidence."
Jev introduces a new shape of LLM
(simonwillison.net)31 points by benwerd 5 hours ago | 7 comments
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Do people not feel like LLM speak (Claudisms) is infecting their own diction? Saying 'a new "shape" of LLM' sits so very poorly.
Broad questions like Is this resume good / score this city will ofcourse be biased but I think jev encourages more granular focused questions like Score this candidates Python experience / Rate this city for its food which then allows you to introduce your own biases in which questions you ask and how you combine their answers.
In this way I think jev like models can be easier to reason about for critical decisions.
Where I understand Jev to be a significant jump is that afaik the confidence scoring is actually derived from the normalised probabilities, and not a continuation in a chain of prediction masquerading as "confidence."