[fud101]

>From what i've gathered, fine tuning should be used to train the model on a task, such as: "the user asks a question, please provide an answer or follow up with more questions for the user if there are unfamiliar concepts."

That isn't what finetuning usually means in this context. It usually means to retrain the model using the existing model as a base to start training.

[Tostino]

I may have not been clear, because I was talking about the RLHF dataset/training that OpenAI fine-tuned their models on which includes a whole bunch of question/answer format data to enable their fine-tuned models to handle that type of query better (as well as constraining the model with a reward mechanism). I'm not saying the fine-tuned models won't contain some representation of the information from the dataset you used to fine tune it. I'm just saying that from what i've researched, it is often not the magic trick many people think it is.

I've seen plenty of discussion on "fine-tuneing" for a different dataset of say: company documents, database schema structure of an internal application, or summarized logs of your previous conversations with the bot.

Those seem like pretty bad targets IMO.

[visarga]

You're right, the RLHF fine-tuning is not adding any information to the model. It just steers the model towards our intentions.

But the regular fine-tuning is simple language modelling. You can fine-tune a GPT3 on any collection of texts in order to refresh the information that might be stale from 2021 in the public model.