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I'm no AI expert but this seems to me like it's overfitting after-the-fact by basically learning from the specific examples and remembering when it's re-run?
by bluelightning2k 3y ago
I'm no AI expert but this seems to me like it's overfitting after-the-fact by basically learning from the specific examples and remembering when it's re-run?
- CuriouslyC 3y agoIt sounds like they're giving it some temperature to allow answer variation, then having it score its own output and taking the best answers. This is akin to common bootstrap methods in statistics that resample the data to estimate a distribution over outcomes based on sample variance. The key in my opinion is the self-scoring procedure, how that is implemented would really control how well this process works. Since it works pretty well here - a 70b non-moe model in striking distance of GPT4 is huge - I'm inclined to say they've happened upon a self-scoring procedure that's good at reinforcing desired traits in the model.
- rybosome 3y agoIt depends on the strength of the seed dataset in diversity of tasks and the initial power of the seed model, I’m guessing. If a wide enough array of diverse input is generated then it may not be overfitting.
- 3abiton 3y agoCan a global model overfit on all the data?