I want to train the model on my data, the model still can answer any question out on the content. is there any way to restrict the model to the given data.
You could try to change the prompt in some way that only allows it to answer to your data. However GPT-3 is generally a non task orriented Language Model. Teherefore the model itself can not be restricted in its answers (other than changing the stopwords). You could however build a small loop around the calling of the QA and check if the question is within your sample of data.
Short answer is no - you can’t restrict a fine-tuned model to answer based on only some of the fine-tuning.
Longer answer: you might not want to anyway. Let’s imagine your content is something dynamic, like a bunch of user profiles from a social network. If you managed to fine-tune it to consistently answer questions about “Cody” based on his profile, you’re going to get out-of-date answers. So now you’re not just trying to limit your scope based on “who’s profile” but over timestamps/history points as well.
I’d encourage you to keep your thinking framed to “what can I pass in the prompt”. That way you’re not trying to fine-tune your model to “know” you’re data - you’re trying to fine-tune your model to get really good at completing prompts that contain fragments of your kind of data.
That’s more do-able and more re-usable extensible.
thanks for your hints
thanks for your answer. Isn’t worthless to fine-tune teh model In case data is dynamic?? I jsut want to keep the user in the scope without distruction in case the input is not clear.