3 ms·
Even if it is overfitting, in some ways this is arguably fine tuning at some subset of the LLM’s capabilities. I can imagine this being a very powerful techniqu
by rybosome 3y ago
Even if it is overfitting, in some ways this is arguably fine tuning at some subset of the LLM’s capabilities. I can imagine this being a very powerful technique with Mixtral.
- TOMDM 3y agoI agree to some extent, though I also wouldn't be surprised if a models ability to evaluate is tied to it's ability to predict. Better predictors evaluate better. I also expect that evaluation ability doesn't grow linearly with prediction ability, so I doubt that a model will be able to fully optimise it's evaluation potential on it's own. Could be wrong though, will be interesting to see. If I'm right on the former and wrong on the latter we could see maybe models self evaluating to an "optimal" state, for whatever it thinks optimal is via self evaluation is anyway. Or this could be a somewhat useful case of overfitting that just refines a few bits.
- CuriouslyC 3y agoI wouldn't characterize it as overfitting, since the evaluation function exists to filter some of the output. This is basically biasing the model towards a subset of the latent space that the evaluation function says is "good" in a very roundabout way.