> I write harnesses that do decision steps and regularly use competing models
Competing models is table stakes for decision making.
While doing the ablations for the "Team of Rivals" paper, I found that the models were very happy to ignore errors[1] when the data it is classifying looks like its own output.
Jev slots into the middle critique in the paper. The only reason we haven't flipped to Jev last week is because we're a HIPAA shop and Typesafe hasn't responded to our emails.
* They are worst at critiquing what has happened in the same model and same conversation
* they are slightly better at critiquing the output of another model it not given all the conversation
* they are better at critiquing a weaker or earloer model by the same vendor
* they are best at critiquing the output of another vendors model
I imagine it is about correlated bias in the training data and regimes etc.
I often run adversarial models in dyadic reasoning. But then have a feedback loop to get that same outcome from a cheap model. A kind of poor man’s distillation :)
Competing models is table stakes for decision making.
While doing the ablations for the "Team of Rivals" paper, I found that the models were very happy to ignore errors[1] when the data it is classifying looks like its own output.
Jev slots into the middle critique in the paper. The only reason we haven't flipped to Jev last week is because we're a HIPAA shop and Typesafe hasn't responded to our emails.
[1] - https://github.com/t3rmin4t0r/critique-evals