I'm the author of [3] where we introduced one of the first natural-language datasets that test graduate mathematics for LLMs, but some of the prompts we took from a copyrighted book and therefore thought about excluding them. Having them in the public dataset would be really nice though, hence I'm keen about your experience.
I'd also be keen to hear how your challenge against the DMCA on sharing LLaMA's weights goes?
[1] https://www.theguardian.com/books/2023/jul/05/authors-file-a... [2] https://arxiv.org/abs/2105.05241 [3] https://arxiv.org/abs/2301.13867
Personally, I'm not worried. It would be a damn shame if academics come under fire merely for trying to operate on the cutting edge of science. None of us were trying to make money; we just wanted to make something interesting.
> I'd also be keen to hear how your challenge against the DMCA on sharing LLaMA's weights goes?
Thanks! I think we might be putting up a website for it soon, if only to explain ourselves. In the meantime – I hate this phrase, since I don't want followers – the only way to keep informed is to follow my Twitter, and perhaps keep an eye on my HN comments.
You'll probably hear about it either way though, since it's a groundbreaking case. No one has tested the copyrightability of ML models before.
I can send you the draft we’ve prepared if you’re interested — drop me an email. But I’ll probably set up a site for this, if only to clear up our motives and expected outcomes.
Copyright lawsuits are usually a case of who has the biggest stamina and hence who has the biggest wallet. Your funding will be a very important part of the outcome, regardless of the legal merits of your defense. You may want to get out of any kind of control of GH because they are strongly connected to OpenAI through Microsoft and hence has a stake in getting rid of any reasonably competent open source LLM.
Make sure you know what you are in for, lawsuits with large counterparties are a rodeo and even if you win they can make your life miserable with endless appeals. You will have to be prepared to spend years on this. Much good luck and if you set up a site do post the link.
Couldn't you use a similar type of argument to say that different implementations of the same software API are basically the same software even though the instructions are completely different each time? And, since they do the same thing (aka compatible), that software implementations can't be subject to copyright for that reason? I don't think that holds up, esp in a pro-copyright country.
Here's one I came up with in case your lawyers can use it. My original goal was a license for proprietary content to be used in LLM's where the creators were worried about verbatim extraction or whether their content was sufficiently mixed in with other data. It was about motivating them to let us train on such data. I'll start with those terms:
"1. Percentage of total data. The copywritten work must not be larger than N% of total, training data put into a model. If it's tiny enough, one might be able to argue it only adds so much weigh to the outputs. What if it's the only data of its type, though?
2. Merged with similar data. The copywritten work must be one of multiple examples of the same types of data. For instance, there might be many examples given to the model about what files are, how to generate them, doing it in Python, and specific examples in Python. When it generates Python code, any or all of this might have contributed to it.
3. Ratio of data, set size to number of parameters. The content owners might want the training data to exceed the number of parameters by a multiplier N. For instance, at least 10GB or 100GB going into a 1G model. The multiplier is 10.
4. Diverse data. The content owner might want a wide range of data on many topics to go into the model. They might even specify certain data sets, a minimum number of topics, or even a number of word vectors per word used (their keywords). Once again, the odds the model is just repeating one piece of data goes down as the number of data and similar words in the model goes up."
So, basically you'd be trying to set a standard where anything the model creators legally have access to that they can put into their LLM's. Are the LLM's then carrying their I.P. or something novel? If novel, we're safe from lawsuits. If LLM's and outputs are not copyrightable, we'd be double safe in that situation.So, maybe use criteria like the above to decide what's novel where anything within certain numbers or combinations would be novel automatically by law or court precedent. What do you think?
Ultimately what convinced me to proceed is that there are immense forces pressuring ML models to become SaaS companies. It's very difficult to offer an ML model for extended periods without being a company. E.g. https://6b.eleuther.ai/ is down. Eleuther failing illustrates just how hard it is –– we were all working as hard as we could to design something that would last a long time, and a long time turned out to be two short years. Contrast that with other kinds of hacking (e.g. webdev, gamedev, hardware...) where the end result lasts basically forever.
So if ML models aren't copyrightable, I think it'll hurt companies a lot more than individuals. In fact the goal is the other way around: to protect individuals. All I did was publish Facebook's own GPL download script to github, and it got DMCA'd. If we don't push back on that kind of behavior now, companies will get used to the idea that they control "their" model –– even when their model is anything but theirs.
- Grammarly. Clear value prop, if weights can't be adequately protected then that's a significant headwind against doing processing on the client.
- Adobe Firefly. Could run locally, they understand the technical challenges well, same headwind.
- GitHub Copilot. Same.
Copyright protection is probably not a single deciding issue in their product strategy but all of those are use cases that would for most users be better run locally as hardware can support that and are not going to because it's too much of a risk. Better to limit distribution and protect as trade secret.
The most powerful force for openness I see has nothing to do with copyright eligibility and everything to do with companies wanting to showcase their research arms to build brand and support recruiting. That leads me to believe it's probably better for models to be eligible for copyright and considered derivatives of all of the constituent training data. In some ways the better parallel is sampling in the music industry. It'll be interesting to see how this plays out.
I think it would be a shame to try to build legislation around the notion of the sass melting pot application of machine learning and in the process destroy all sorts of other use cases.
No, because we already do not treat all work as copyrightable. A plumber doesn't get copyright on his piping job. It has to be original enough. So while your own skill might be original enough to warrant copyright, distilling it into a model might not.
Yes. So it's like creating a machine that can create art.
Perhaps it shouldn't be copyrightable, but patentable. I think I would be OK with ML models (weights) being patentable rather than copyrightable.
In my opinion the most ethical outcome would be that they are on the hook for the cumulative cost of the copyright they violated. That way authors would come out ahead instead of having their rights trashed 'because it's too late anyway'.
At some point this stuff will have to get tested by making its way up the appeals stack in the US, and IMO there is only a minuscule chance that will result in Google, MS, and Meta getting slapped with anything more than a token fine (my bet is it won't even be that), let alone paying every person who ever wrote anything that was used in these datasets for copyright violations, which would basically be everyone.
Examples:
Japan: Article 30-4 of the Japanese Copyright Act. No special action on the part of companies is necessary for compliance. All models are legal so long as their output is legal.
The UK: s.29A of the Copyright, Designs and Patents Act 1988 (CDPA). Models must be trained by non-profit research institutes, and can then be used by anyone (including for profit entities); similar to the Stable Diffusion model.
The EU: Articles 3 & 4 of the Directive on Copyright in the Digital Single Market (CDSM). There are no restrictions on non-profit TDM, same as the UK. For-profit TDM is exempted from copyright so long as the data harvesting process respects an "opt-out" process, where specific contractual forms/disclosures of opting out of inclusion in the training data are respected.
Singapore: Articles 243 & 244 of the Copyright Act. No special action on the part of companies is necessary for compliance. All models are legal so long as their output is legal.
I think there's a strong argument for a Fair Use defense, given the size of the models versus the size of the training sets, as well as the gulf in intended use: an AI model doesn't compete with e.g. a book. Obviously we'll have to see if play out in court to find out.
From a scientific point of view this is very problematic because few safeguards exist that guarantee that the dataset is not tampered with (as is the case if you'd upload it to Zenodo, which providea some guarantee of immutability).
How about trying to upload the Pile to Zenodo? Only half-joking :D
I’ll read over your essay and give it some thought. There are a bunch of subtle aspects to consider; I’ve been thinking it over for about four months now and still haven’t covered all the territory yet.
It feels like this may be one of the most important decisions going forward — both from an intellectual property point of view, and an individual rights perspective. E.g. you mention that it’s civil disobedience to share the weights, but it feels like if someone is claiming to do open science (LLaMA), sharing the research materials is the minimum requirement. Plus look how it’s benefited them; they’ve captured most of the open source LLM mindshare. So it seems likely that this will lead to more open source work in the long run, not less.
Feel free to chat! You can DM me on Twitter or email me. I’ve been in the hospital with my wife for 7 weeks, with two to go, so I’ve been a bit less responsive than I usually am.
I'l get an LLM to summarise your essay...
We will see that anyway. All the code I work on commercially is copyrighted and yet a trade secret. Existence of copyright (with the exception of copyleft, but that's subversion) didn't help software to be open sourced.
IMHO allowing models to be copyrighted is basically 18th century enclosures again.
What do you mean by that? Do you continuously copyright the changes?
Besides, it's already pretty unambiguous that weights are not copyrightable: they're a result of a mechanical process. The only original creative input that goes in to the weights is the unfathomable amounts of content scraped from other sources that aren't the authorship of the models. The objective of the gradient descent is simply minimizing loss on the training data.
Facebook doesn't own the llama model weights any more than the Bridgeman Art Library practically owns the paintings of European masters because they made quality scans of them. ( https://en.wikipedia.org/wiki/Bridgeman_Art_Library_v._Corel.... ), or any more than Rural Telephone owns the phone directory ( https://en.wikipedia.org/wiki/Feist_Publications,_Inc.,_v._R.... ).
Trying to make model weights copyrightable is going uphill, and I don't see how you get there without first establishing that the these LLM are unlawful derivatives of a countless number of copyrighted works along the way. Doing so would probably create a immediate monopoly for legally created LLMs for the hand full of corporations with quasi-monopoly content hosting services (facebook, google, etc) that can (and/or already have) stuffed licensing into their terms of use.
Do you want a cyberpunk dystopia? I think creating an AI monopoly is how you get a cyberpunk dystopia -- and the two ways we end up with one is either outright restrictions on private development of AI like some have been lobbying for and the other is the extension of copyright so that only a few entities can get access to enough of other people's data at a low enough cost to train them.
Assuming you haven't taken a photo a second for your entire life, then I suspect you'll struggle to make something even close to what's available publically, due to lack of training data.
That said, if you've got a million photos you could probably do some pretty interesting things with very large scale fine tuning, or if you know many other people who have similar stockpiles of photos you may be able to get an entry-level dataset together if you all pool it.
Does anybody have a link to a relevant discussion here? I would like to read about the creative process that goes into defining model weights, and how it differs from the mechanical output of running the training algorithm.
Boy they'll be mad once they learn about Huggingface distributing thousands of LLama fine tunes with full weights.
As far as I know, this was the first academic contribution from a discord collaboration to ML. Back then discord was barely used for ML at all, though nowadays of course the largest discord in the world is midjourney.
There were a bunch of interesting stories from those days. We almost didn’t release at all (or at least the books component) because of fear of copyright backlash. Turns out no one cared, and then suddenly today the world cares a great deal.
As a side note, I’ll be participating in a legal action against Meta for the purpose of making ML models uncopyrightable: https://twitter.com/theshawwn/status/1641804013791215619?s=6.... They DMCA’ed one of my repos distributing LLaMA, so we fought back and challenged the idea that weights can be copyrighted at all. This seems like the best outcome for hackers and individual researchers, for a few reasons. It’s also one of the most ethical outcomes; since ~no one trains on data that they own, they shouldn’t own the resulting model.
One last thing. The Pile would’ve been far less relevant without the wonderful assistance of The Eye, a group of people who archive all kinds of things. They’ve hosted the datasets for years now. And although it seems strange to say that dataset hosting could make or break The Pile, back then there was nobody else willing to host us. https://the-eye.eu/