Fine tuning is just more training -- so it's definitely possible to teach the model facts this way too.

In practice we've found that it's a bit of a balancing act to teach the model the new knowledge without destroying existing knowledge, but it's just a matter of tuning the parameters carefully. We're also researching whether we can fine-tune a brand new expert in a MoE model like Mixtral, I've also seen work on fine-tuning just a fixed set of weights. I'm sure there will be more developments in this space soon.

In terms of how you refer to new knowledge and not base knowledge, like many things in LLMs, you just ask the LLM :-) For example, if you look at this session https://app.tryhelix.ai/session/62905598-b1b7-4d93-bc39-5a93... and click "Show Info" at the top, you can see the system prompt is:

"You are an intelligent chatbot named Helix that has been fine-tuned on document(s) e1ef2e896c in document group 62905598b1. The document group contains 1 document(s). The user will ask you questions about these documents: you must ONLY answer with context from the documents listed. Do NOT refer to background knowledge."

It does a pretty good job at this, although I'm sure there are ways to improve it further.

Referencing the specific document IDs in the fine-tuning was an innovation that has really helped us.

In terms of training time, yeah - 5 minutes on a news article, 10 minutes on a typical length paper. Pretty usable. We're experimenting with reducing the number of epochs and increasing the learning rate to make it faster at that too.