Thread

1. NeumannGod · 1791196076 · focus · HN ↗

   This is a false comparison. What Jev does is fundamentally different from what LLMs are doing as a judge.

   For a quick primer, Jev is able to provide confidence scores on its classification, i.e. it is able to calibrate how well it is able to predict. Being able to predict in a distribution is different from being able to calibrate confidence of the predictions which should happen from the question or domain distribution from which the decisions are predicted - being able to do that is tough and is not same as using LLMs logit probabilities which are predictions in the vocab space. Though both are loosely correlated and might converge as LLMs keep getting better, the former is a much stronger decision-making signal than the latter. Jev not beating LLM-as-a-judge might be due to various other reasons such as world knowledge etc, but Jev as a concept will always provide more reliable decisions / outputs than LLM-as-a-judge giving a scalar score.

   1. yieldcrv · 1791199145 · focus · HN ↗

      for the uninitiated:

      LLM’s are not able to give confidence scores, they make them up.

      Your AI driven app is making that up. Your product manager and executive team’s demand for confidence in the UI is a totally fictional cosmetic telling them nothing. Your company sold bullshit confidence to your clients.

      I’ve done this for many organizations that “formed a new team to work with the CTO on their AI strategy”, and the trappings are the same

      You can have an LLM tell you how much of a schema it was able to get information about. And derive a “confidence” or level of compliance from the completeness of the schema

      But this is layers upon layers of cruft that a classification model wouldn’t need

      1. andy99 · 1791201495 · focus · HN ↗

         Softmax over logits doesn’t give calibrated probabilities either as a rule. I don’t want to comment specifically on Jev but as a rule it’s very hard to get good calibration because it’s somewhat in tension with minimizing training loss for neural networks, e.g. Guo et al (2017) <a href="https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;1706.04599" rel="nofollow">https:&#x2F;&#x2F;arxiv.org&#x2F;abs&#x2F;1706.04599

         While I know there are ways to improve calibration, I’d personally want to see a lot of evidence the probabilities were actually more meaningful before trusting them. I agree of course that asking an LLM to provide a confidence estimate is meaningless.

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