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You don't really need understanding if you ask (or train, or fine-tune) the AI to produce a formalized answer, and then write a loop that feeds back the compile
by t_mann 3y ago
You don't really need understanding if you ask (or train, or fine-tune) the AI to produce a formalized answer, and then write a loop that feeds back the compiler output until it's correct. That doesn't guarantee that it's the answer that you want, but it's guaranteed to be correct, so it is something. You can also use this approach to produce more training examples, if you have time you can manually check and annotate whether the result is what was originally asked for, and then hope that the training improves the accuracy of the initial answer (avoiding the loaded term 'understanding' here). This might substantially reduce the amount of work that needs to be done manually after the first step.
If I was doing a Math degree right now, that's exactly the kind of stuff I'd want to do for my thesis, kind of jealous tbh.
- jhanschoo 3y agoThis suffers from the problem of sparse rewards. You need a better reward that gives value to intermediate results or special-case answers, since too few states are correct.
- t_mann 3y agoIt's not about training the model. You just input the error messages together with an appropriate prompt and ask it to fix it. Iterate until it verifies successfully (or stop after you've reached ~1-2x content window length). Once you have produced examples of correct math that way, you can use those to fine-tune the model.