We are keeping a list of AI/ML related links from research to more accessible items, hoping to share some of the more accessible posts with a wider community!
It almost certainly can hallucinate the known function list, but it's unlikely since the model necessarily needs to have a strong conception of the functions to interface consistently.
That's speculation, of course, since GPT is a closed model, but, based on how like-models are known to work, we know that there's just one single "slot" for input to flow into without any backdoor pathways for specially priviledged input.
pretty much this, and unlike claude a month or so ago GPT has been very consistent with context its been given over anything it might come up with on its own.
its consistent enough I can build applications with low enough error rates that I feel i can sell more than a chat window and not worry much about Bobby Tables.
Not wrong, but not entirely right, it does not "know" and is not fundamentally capable of "analysis" they way we describe it. Its a weighted tokenmap, what makes it special is how those weights were derived. There are papers on self-reflection that are essentially processes in mapping those weights. You can assess the probabilities of your token response in the API as well.
What's interesting is that sufficiently large models demonstrate the ability to build upon themselves using similar techniques applied in training and in fine tuning right in the prompt. This means you can use a combination of reflective conversation and embedding to create prompts that act like fine-tuned agents. Great for fast prototyping and cheaper than a fine tune run!
It does not genially hallucinate with data it has, the LLM has to know about the commands it has from the plugin and this can only be done by feeding that data into context.
I suspect they can't put this info in a system level message as it could cause extra confusion and create an injection vector for un-checked plugins. So instead they shove the command set in a user or assitant message which a user can get access to just by asking. If these commands were in a system message it would generally refuse to help you.
tl;dr - it does not hallucinate data its been given, just when you ask it to "come up" with things.
I'm not sure if I'd call that reverse engineering any more than using a web browsers View Source feature.
But it's interesting how it works behind the scenes and that only way to get these models to interface with the external world is by using the natural language interface and hoping for the best.
First step of reverse engineering is taking the case off. Like most, we just take the text of the post this came from, i think source got lost due to reddit move.
How technical does this community want to get? I have a growing list of research papers dense with math if you want it.
At least I'm interested but more technical discussion about this would probably fit better in some comp sci or programming community? Though most of those are a bit hostile to the LLM related topics these days because of all the hype and low effort spam.