Hello Dolly: Democratizing the magic of ChatGPT with open models

Alpaca was made to fine-tune LLaMa, however they also released their dataset they used to do this, and it looks like Dolly is this dataset applied to GPT-J, and does not use LLaMa itself.

The legal relation between models and training data sets seems murky; of course, with the build tooling, you can also substitute in another instruction-following training set if you want to avoid licensing issues with the Alpaca set, whereas if you aren't concerbed with them, you can just blaze ahead.

Can't they also release the fine-tuned weights as non-commercial as well?

or you can raise $30,000,000 right now and worry about the copyright infringement lawsuit in 2026 or never.

The implication being that you're only "democratizing" something if people can make money off of it?

Was the Alpaca dataset being licensed as non-commercial only the reason you aren't releasing the weights? Is it possible to just release them under the same license?

AI being tuned to be "safe" by an exceedingly small set of humans is the thing we should be afraid of. It's the effective altruism effect: if you bombard people enough with "safety" and "alignment" speak, they will look past the fact that you're mainly interested in being a monopoly. My bigger conspiracy theory is that Bill Gates getting behind "AI alignment" is a calculated move to get people to look past Microsoft's unilateral involvement.

Like giving away a website template without the demo content, it's perfectly normal.

Unless I am misunderstanding (?), this seems like an overgeneralized lesson. There are many key differences between these situations that make such a connection unlikely. Could you explain your reasoning?

It seems like there easily could be. What if some of the data they trained it on didn't exist anywhere else except in the training set, and was put there specifically for this purpose? For instance they could have taught it a few poems that don't exist anywhere else. If you can coax the LLM of unknown origin into reciting those poems back to you, you know where it came from.

There's precedent for "whatever you can get away with" in tech companies, but establishing a culture of that at the start of this new big change could end up undesirable for most people.

For example, it could relieve demand for more legal and sustainable ways, until it's too late. (Look at the history of digital entertainment media piracy and DRM and legislation, for example. Or look at the history of software piracy, where some big companies seem to actually want their product to be pirated, partly because it builds a bigger moat against competitors, and they can legally strongarm some of those pirates later.)

If you have a use case or a bunch of disposable income then go with the “bitter” one.

Isn't there? I'm certainly not sure, based on the results published over the last weeks and months.

The giant GPT-{3.5,4} models show that if you make the model big enough and throw enough data at it you can produce an AI capable of conversing on basically any topic, in dozens of languages. There are plenty of different takes on how near-human its abilities are on specific tasks, but it's worth stepping back and appreciating how super-human the breadth of this knowledge is.

But it's also not clear if a mega-model is anything close to the most efficient way of storing knowledge. After all, you don't need to memorize every fact in Wikipedia if you know how to effectively search it.

And we're currently seeing a daily explosion in these capabilities. Today's flavor is interfacing with Wolfram, but we've also seen web searches, python coding, etc. That, I think, it the real superpower that comes out of this: you or I can answer a question by "doing a web search" or "query a database" or "use wolfram" or "develop a python program that finds the answer" However, an AI could do tasks like this just by "thinking" about it. Maybe it would be as natural as we find blinking.

That to me is the real breakthrough in stuff like Alpaca -- start with a mega-model and prompt it with something like: "After this paragraph, you are going to be speaking to a AI model similar to yourself but much more primitive. Its task will involve interfacing with English speakers, so converse with it only in that language. It has access to the same {X,Y,Z} APIs you have so any time it has trouble answering a question, prefer to give hints about how it could find the answer using those APIs rather than providing the answer directly yourself. Only give an answer directly if it repeatedly fails to be able to answer it by using an API. I've provided a large set of standardized tests used by humans at this URL -- start by asking it questions intended for a preschool-aged child. Each time it is able to answer new questions at a given level correctly 99% of the time increase the material's level until it is able to achieve that score on a test designed for a Computer Science PhD candidate"

How large would the "student" model have to be to succeed at this deep but narrower task? I think the answer right now "we have no idea". However if the model has the advantage that it can rely on external knowledge and tools from the start (and is rewarded by the "teacher" for doing just that) I bet it'll be a lot smaller than these mega-models. Sure, you wouldn't be able to disconnect the "student-AI" from its APIs and expect it to converse with you in Hungarian about the history of yacht design, but that might not be a capability it needs to have.

My personal hunch is that we're going to find these "AI-taught specialist AI, with API access" models will be a lot smaller than most people are expecting. That's the moment when things REALLY change: instead of pairing a human with a mega-model AI, if specialized models are cheap someone can say "spin up 100K expert-programmer AIs and have them supervized by 5K expert-manager AIs and have them build XYZ"

Or if you need it to work on an existing task you'd specialize further -- you'd go to your AI vendor and say "I'd like to license the weights for your expert-programmer model, but first have it read these 200 books I consider important to my problem domain and then show it every commit ever made by a human to my git repo and every design document I have"