I believe you could generate an endless supply of valuable synthetic data if you bring multiple LLM models into a conversation with each other and use a recursive process where the models criticize their own work, pretending to be different fictional and real characters (professor of physics vs. electrical engineer, etc.) who offer their independent feedback on ideas discussed. I think these can be combined with autoGPT and babyAGI with the goal of testing each other and discovering their own strengths and weaknesses. Then they could seek out new data that would bridge the gap in their understanding and maybe autonomously train new models for that purpose. I noticed chatGPT and GPT4 routinely states that they are unable to do certain things that they really can. You just have to get the model to assume the identity of someone who knows how to do the task, and suddenly it does the thing just swore it could not do. What are your thoughts?
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