Agree, it's confusing. I hear people discussing which model to use for what tasks and my head starts to spin because there are so many properties to account for and it's generally hard to reason about LLMs.
"Speed, quality, price: pick two". I think I'd rather see a choice like that than the current convolution of $-per-token, sub quota, latency, simulated reasoning level, model size, context size, etc, etc.
Agree. In fact Jev does reasonably well in both tests. In the article, the assertion is hedged, and followed up with a sentiment I liked:
> decision models like Jev do not reliably outperform [other] models in speed or accuracy. However, they rightly refocus industry attention on lightweight, task-specific inference paradigm that more closely resembles predictive machine learning
(I also noticed some issues with bolding in Table 4 that downplay Jev's performance a little.)
I agree that there are differences between Jev and LLM-as-a-judge (e.g. Almeida's assertion that LLM probabilities have been irrevocably biased by RLHF), but I am not sure about your description of 'confidence'. Perhaps I misinterpret you or the docs, but I understand it as simply being computed from the probability distribution: https://docs.typesafe.ai/confidence#how-confidence-is-calcul...
"Speed, quality, price: pick two". I think I'd rather see a choice like that than the current convolution of $-per-token, sub quota, latency, simulated reasoning level, model size, context size, etc, etc.