They just aren't quite close. Maybe you guys are asking shallow questions, but I'm asking subject matter questions and this is just not true.

It would be great if we could have detailed QA evaluation to show this, but of course then the open source people would train their models on it as a fine-tuning datasaet.

Train it on specific subject matter and not waste space on things like “what year did Jack Nicholson beat on that dude’s car with a golf club?”

I mean… the horror.

That's just not how these models work.

You’ll have to forgive them, they’re probably trained mostly on HN posts. ;)

What are the newer models? I am testing them in batteries across complex tasks, so far vicuña is the most flexible but they all choke on reflective instructions (I. E. Knowledge to extract is not in the model but in the user text)

I very interested in this usecase - where should I go on the interwebs?

original paper: https://arxiv.org/abs/2205.00445

langchain agents are a good starting implementation.

you can build your own prompt and get the ai to work by iself hallucinating tools, which may be cheaper to test out than going back and forth with an agent manager. not as accurate, but you can still extract useful work, i.e. https://i.imgur.com/AE4R3dR.png (gpt-35-turbo is traditionally failing this task completely, prompt get it to work at it)

these prompt all require the model to work off data within the prompt within the first shoot. model require a degree to introspection for that to work.

The way OpenAI has set their models up for prompting and follow-up is where their real advantage is. It's very hard to take a model you can run locally and just go "Hey, write me a powershell script to convert all these files into this format. Split them up so they're no more than an hour each. Ok, change it to also denoise them."

Are you talking about the RLHF? That's where Vicuna/Alpaca and other finetuning help a lot.

There's also already tools for conversation flows (which just means you prepend the conversation history to the prompt).

I'm not saying the performance is nearly as good, but the actual workflow does already exist and is massively improving. The interesting part to me is that this finetuning can be done in a couple few hours on a consumer gpu (4090).