Overfitting when giving samples in prompts

I’m trying to use few-shot learning to try and get GPT to generate answer to a question in a specific manner.

However, the generation for a question completely unrelated to the sample questions is still being influenced by the sample answers they have.

Anyone who has encountered this and has a prompt tweak that might help sort this out


1 Like

Are you talking about fine-tuning a particular base model @udm17 ?

Not fine tuning. Using samples along with a prompt to generate the answer for a question. Sadly can’t share more info as it is for a propriety software.

What settings are you using? temp, freq_penalty, etc

Temperature - 0
Freq Penalty - 0
Top P - 1
Presence Penalty - 0

In my experience, the lower you go with temperature, the more likely it is to overfit/repeat… If you can’t raise the temperature, try moving frequency_penalty up a bit… but slowly… 0.05 at a time maybe?

Good luck!

Cheers Paul.

This is something I have been tinkering with a bit, hopefully can find the sweet spot soon. I want the answer to be deterministic to a certain extant, so have been using low temperature and that definitely has led to overfitting.

Hi @udm17

Which model are you taking about?

It’s a secret?


Base model I’m using currently is davinci003 and has recently started using the gpt3.5 turbo. Am not too sure whether I should use a fine-tuned davinci would solve my problem.

Fine-tuning is the best OpenAi tool available for better model fitting.



1 Like