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"Google said in a statement: “Quality raters are employed by our suppliers and are temporarily assigned to provide external feedback on our products. Their rati
by iandanforth 1y ago
"Google said in a statement: “Quality raters are employed by our suppliers and are temporarily assigned to provide external feedback on our products. Their ratings are one of many aggregated data points that help us measure how well our systems are working, but do not directly impact our algorithms or models.” GlobalLogic declined to comment for this story." (emphasis mine)
How is this not a straight up lie? For this to be true they would have to throw away labeled training data.
- Gracana 1y agoThey probably don’t do it at a scale large enough to do RLHF with it, but it’s still useful feedback the people working on the projects / products.
- zozbot234 1y agoMore recent models actually use "reinforcement learning from AI feedback", where the task of assigning a reward is essentially fed back into the model itself. Human feedback is then only used to ground the training, on selected examples (potentially even entirely artificial ones) where the AI is most highly uncertain about what feedback should be given.
- _2d30 1y agoBecause they are doing it to compute quality metrics not to implement RLHF. It’s not training data.
- teiferer 1y agoKey word: "directly" It does so indirectly, so it's a true albeit misleading statement.
- skybrian 1y agoIt's not part of the inner feedback loop. It's part of the outer feedback loop that they use to decide if the inner loop is working.
- yobbo 1y ago> For this to be true they would have to throw away labeled training data. That's how validation works.
- jfengel 1y agoIs there a reason not to use validation data in your next round of training data? Or is it more efficient to reuse validation and instead get more training data?
- parineum 1y agoYou'd have to recreate your validation if you trained your model on it every iteration and then they wouldn't be consistent enough to show any trends
- jfengel 1y agoI'd have thought that if you kept the same validation you'd risk over fitting. Clearly that does make it hard to measure. I'd think you'd want "equivalent" validation (like changing the SATs every year), though I imagine that's not really a meaningful concept.