The correction prompt is very important, it will definitely determine the outcome of the process, a bad correction prompt will obviously lead to a garbage result.
Training in steps with different prompts might be of value. First step might be to fix contradictions, then factual errors if that is an issue. This is an idea that I got when viewing the he output of LLaMA, it often contains contradictions (eg. an example I have seen is "Peter is a boy and he is part of the Gama sorority"). Asking it to fix those types of issues should be a first good step.
But I suspect that this type of training would need to be mixed with original training data. Otherwise the restructuring in the model caused by the new training would most likely garble the rest of the model.
The correction prompt is very important, it will definitely determine the outcome of the process, a bad correction prompt will obviously lead to a garbage result.
Training in steps with different prompts might be of value. First step might be to fix contradictions, then factual errors if that is an issue. This is an idea that I got when viewing the he output of LLaMA, it often contains contradictions (eg. an example I have seen is "Peter is a boy and he is part of the Gama sorority"). Asking it to fix those types of issues should be a first good step.
But I suspect that this type of training would need to be mixed with original training data. Otherwise the restructuring in the model caused by the new training would most likely garble the rest of the model.