uhhhhhhhhhh okay my idea was to have two different structures that formed confidence metrics around the data, then to combine the confidence metrics so as to improve the structures, in feedback. would this work with limited labels? the model generates output, possibly with confidence metrics. the labels are few and correct, and provided in an order over time. something's missing here. there's only one algorithm. (part of the issue is i don't remember my idea). for inclusion, here is a very basic idea i haven't heard mentioned much: the model could treat each label as a new train/test set, and keep training on the old labels until it improved at the next labels. this will only work if done a certain way, it doesn't work as an overall strategy because the model isn't good at 'going back' and 'undoing' things it learned wrongly. the 'going back' and 'undoing' is often addressed by having a lot of diverse data. lots and lots, as diverse as possible. if we could consider the properties the model is learning a little, we could maybe emulate something similar on a small scale. for example, just a single word from each set of labels could be considered, and the model would treat surrounding words as test material. this aligns things a little better. mm. anyway, it seems to help to learn to finetune one of these speech-to-text models around data a little. maybe i can see if i can pull that off, just as an experiment.