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It's not so simple. Garbage in garbage out. But if you can get a validation signal in there, maybe using an external tool, a game, an outcome of some sort, then
by visarga 4y ago
It's not so simple. Garbage in garbage out. But if you can get a validation signal in there, maybe using an external tool, a game, an outcome of some sort, then maybe it can work. For example generate code, and then run it to see if it passes tests - this is a good filtering signal.
- cubefox 4y agoThese guys did that by letting a model generate unit tests and then let the interpreter check it: https://arxiv.org/abs/2207.14502 https://arxiv.org/abs/2207.14502
- sebzim4500 4y agoI don't think you necessarily need any validation signal to improve performance with synthetic data. Here's an example I posted further up: > Suppose you want to optimize the ability for an LLM to do arithmetic. You prompt the LLM to generate a bunch of arithmetic questions, prompting it to show its working. Then you take that output, remove the intermediate steps, and train on the results.