You wouldn't train a LLM on a corpus containing copyrighted works without ensuring you had the necessary rights to the works, would you?
And before you knee-jerk "it's a compression algo!", I invite you to archive all your data with an LLMs "compression algo".
Neither am I, yet, I am still capable of reproducing copyrighted works to a level that most would describe as illegal.
> And before you knee-jerk "it's a compression algo!"
It's literally a fundamental part of the technology so I can't see how you call it a "knee jerk." It's lossy compression, the same way a JPEG might be, and simply recompressing your picture to a lower resolution does not at all obviate your copyright.
> I invite you to archive all your data with an LLMs "compression algo".
As long as we agree it is _my data_ and not yours.
Compression yes, but this is co-mingling as well. The entire corpus is compressed together, which identifies common patterns, and in the model they are essentially now overlapping.
The original document is represented statistically in the final model, but you’ve lost the ability to extract it closely. Instead you gain the ability to generate something statistically similar to a large number of original documents that are related or are structurally similar.
I’m just commenting, not disputing any argument about fair use.
And is technically copyright infringement outside fair use exceptions.
Would Einstein would have been possible without Newton?
But as things are, the megacorps are training their LLMs on the commons while asserting "intellectual property" rights on the resulting weights. So, fuck them, and cheers to those who try to do something about this state of affairs.
At the start of the tape, there was a copyright notice forbidding the VHS tape from being played at, amongst other places, schools.
Copyright rules are a strange thing.
as an example: saying “i really like james holden’s inheritors album for the rough and dissonant sounds” isn’t covered by copyright.
if i reproduced it verbatim using my mouth, or created a derived work which is noticeably similar to the original, that’s a different question though.
in your example, a derivative work example could be akin to only quoting from the book for the audience and modifying a word of each quote.
“derived” works are always a grey area, especially around generative machine learning right now.
most other places use fair dealing which is more restrictive https://en.m.wikipedia.org/wiki/Fair_dealing
Unless all LLM are a ruthless parody of human intelligence, which they may be, the legal issues will continue.
- Addenda -
For the interested parties, the law states the following [0].
Notwithstanding the provisions of sections 17 U.S.C. § 106 and 17 U.S.C. § 106A, the fair use of a copyrighted work, including such use by reproduction in copies or phonorecords or by any other means specified by that section, for purposes such as criticism, comment, news reporting, teaching (including multiple copies for classroom use), scholarship, or research, is not an infringement of copyright. In determining whether the use made of a work in any particular case is a fair use the factors to be considered shall include:
1. the purpose and character of the use, including whether such use is of a commercial nature or is for nonprofit educational purposes;
2. the nature of the copyrighted work;
3. the amount and substantiality of the portion used in relation to the copyrighted work as a whole; and
4. the effect of the use upon the potential market for or value of the copyrighted work.
So, if you say that these factors can be flexed depending on the defendant, and can be just waved away to protect the wealthy, then it becomes something else, but given these factors, and how damaging this "fair use" is, I can certainly say that training AI models with copyrighted corpus is not fair use in any way.
Of course at the end of the day, IANAL & IANAJ. However, my moral compass directly bars use of copyrighted corpus in publicly accessible, for profit models which undermine many people of their livelihoods.
From my perspective, people can whitewash AI training as they see fit to sleep sound at night, but this doesn't change anything from my PoV.
[0]: https://en.wikipedia.org/wiki/Fair_use#U.S._fair_use_factors
The nature of copyright and plagiarism boils down to paraphrasing, and so long as LLMs sufficiently paraphrase the content it's an open question whether it's copyright infringement and requires new law/precedent.
So the fact they are earning money is a red herring unless they are reproducing the exact same content without paraphrasing (with exception to commentary). E.g. they can quote part of a work while commenting on it.
Where they have gotten into trouble with e.g. NYT afaik is when the LLM reproduced a whole article word for word. I think they have all tried hard to prevent the LLM from ever doing that to avoid that legal risk.
How many books can you read, understand and memorize in T time, and how many books an AI can ingest in the T time?
If we're down to paraphrasing, watch this video [1], and think again.
Many models, given that you ask the correct questions, reproduce their training set with great accuracy, and this is only prevented with monkey patching, IIUC.
So, it's still a big mess, even if we don't add copyrighted corpus to the mix. Oh, BTW, datasets like "The Stack" are not clean as they claim. I have seen at least two non-permissively licensed code repositories inside that dataset.
I am curious about the video, but am not compelled to spend 24 min watching it when you haven't summarized its thesis for me. The title of the video makes it seem adjacent at best to the points I was making. (Some automated flagging system =/= actual law)
Another weird carve-out for copyright law in the US: parody. Honestly, I don't know if other jurisdictions allow parody in the same protected manner.
Germany: https://www.gesetze-im-internet.de/urhg/__51a.html (Though this explicit carve-out is a recent development, though generally speaking parodies were allowed even under the previous version of the law.)
Nope :-), just a normal citizen, but sometimes I am curious enough to look up a law, plus sometimes I need to refer to/look up some law in my day job as a civil engineer, too.
When you need to do that, it's not too hard to stumble upon the existence of that page through some web searches, plus the German Wikipedia often links to that page, too (as well as to some alternative platforms run by private entities, which sometimes provide some added value, e.g. buzer.de provides change history since 2006, too, other pages link relevant court decisions, etc. etc. – but gesetze-im-internet.de is the official page run by the federal government itself).
> In determining whether the use made of a work in any particular case is a fair use the factors to be considered shall include:
> * the purpose and character of the use, including whether such use is of a commercial nature or is for nonprofit educational purposes;
> * the nature of the copyrighted work;
> * the amount and substantiality of the portion used in relation to the copyrighted work as a whole; and
> * the effect of the use upon the potential market for or value of the copyrighted work.
LLMs are not humans. They’re essentially a probabilistic compression algorithm (encode data into model weights/decode with prompt to retrieve data).
Depending on how much music you've listened to, you very well may have "downloaded terabytes" of it into your brain. Your argument is specious.
There is little mystery to how large language models function and it's clear that their output is parroting back portions of their training data, the quality of output degrades greatly when novel input is provided. Is your argument that people fundamentally function in the same way? That would be a bold and novel assertion!
If this were true, then you would be able to identify the specific work being "parroted" and you'd have a case for copyright infringement regardless of whether it was produced by an LLM at all. This isn't how LLMs work though. For instance, if an LLM's training data includes the complete works of a given author and then you prompt the LLM to write a story in the style of that author, it will actually write an original story instead of reproducing one of the stories in its training corpus. It won't be particularly good but it will be an original work.
It also isn't obvious whether or not, or to what degree, LLM training works differently from human learning. You yourself acknowledged that there are "many open questions" about how human learning works, so how can you be so confident that it's fundamentally different? It doesn't matter anyway because you can still apply the exact same standards to LLM output to judge whether it infringes copyright that you would to something that was produced by a human being.
i listen to it on apple music.
i pay money to apple for this.
some of that money that i pay to apple goes to the rights holders of that music for the copying and performance of their work through my speakers.
that’s a pretty big difference to how most LLMs are trained right there! i actually pay original creators some money.
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i am a human being. you cannot reduce me down to some easy information theory.
an LLM is a tool. an algorithm. with the same random seed etc etc it will get the same results. it is not human.
you put me in the same room as yesterday i’ll behave completely differently.
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i have listened to way more than terabytes of music in my life. doesn’t mean i have the ability to regurgitate any of it verbatim though. i’m crap at that stuff.
LLMs seem to be really good at it though.
If I can hire an employee who draws on knowledge they learned from copyrighted textbooks, why can't I hire an AI which draws on knowledge it learned from copyrighted textbooks? What makes that argument "wacky" in your eyes?
Unlike a person, an large language model is product built by a company and sold by a company. While I am not a lawyer, I believe much of the copyright arguments around LLM training revolve around the idea that copyrighted content should be licensed by the company training the LLM. In much the same way that people are not allowed to scrape the content of the New York Time website and then pass it off as their own content, so should OpenAI be barred from scraping the New York Times website to train ChatGPT and then sell the service without providing some dollars back to the New York Times.
You're either going to get: it's a technological, infinitely scalable process, and the training data should be considered what it is, which is intellectual property that should be being licensed before being used.
...or... It actually is the same as human learning, and it's time we started loading these things up with other baggage to be attached to persons if we're going to accept it's possible for a machine to learn like a human.
There isn't a reasonable middle ground due to the magnitude of social disruption a chattel quasi-human technological human replacement would cause.
Can you help me to understand the term "chattel" as you used it? I never heard the term before I read your post, and I needed to Google for it: <<
(in general use) a personal possession.
(in law) an item of property other than freehold land, including tangible goods ( chattels personal ) and leasehold interests ( chattels real ). >>
Namely, a magic, technologically reproducible box that can be applied almost as effectively as a human hireling, but isn't a human hireling is near infinitely more desirable in a capitalist system since the blackbox is chattel, the hired human is not. The chattel has no natural rights, no claim to self sovereignty, and is an asset that is legally extant by virtue of the fact it is owned by the owner.
Chattel that are flexible enough to replace the legal burdens incurred by hiring a human to do the same job, will naturally be converged upon due to the capitalistic optimation function of minimizing unit input cost for output over dollars and potential dollars as expressed through legal exposure.
Imagine you had two human like populations. One made of plastic that aren't considered humans but property. I.e. are chattel. Then you have a bunch of people with all the baggage that comes with it.
Hiring people/employing people is hard. Particularly in the U.S. and other jurisdictions where a great deal of responsibility for actually implementing regulations/ taxation/immigration and such is tacked onto being an employer/being able to hire.
As the gap between the capability of the chattel population closes in on the human population, the more economic and workload sense it makes for the system to improve the chattel population under our current optimization strategy, (given no pre-emptive work to cut off externality dumping). Humans are messy and complicated to work with. Often unpredictable. Chattel are easy to account for; especially when combined with "technical restraints". You have to fundamentally engage in negotiation with another human being to get them on board with working for you. You buy the chattel, and that's that. The chattel has no grounds to refuse service. Socially speaking, we don't even recognize it's outputs as carrying any social weight, or resistance as anything but malfunctions.
Economics is the science around using access to resources as a means to get other people to work with you. Being chattel means you can cut out entirely all that complexity. You are resource. Not people.
Unironically, we need to have an answer to whether or not we are going to consider a sufficiently complex function imitator as something that requires a classification above "chattel" or controls around how we apply it in order to not self-destruct the economic equilibria in which we purport to exist. Because all it takes is removing or sufficiently obstructing the flow of value down from individuals who accrete the most of these wunder-chattel to render things so top heavy, most of the constraints/invariants of our socioeconomic systems as we know them become invalidated.
That does not bode well for anyone.
> The point of copyright is to protect an exclusive right to copy, not the right to produce original works influenced by previous works.
> If an LLM produces original works that are influenced by the training data, that is not a violation of copyright.
This isn’t how LLM’s work though. Samples are just that, literal samples that are copied from one work to another verbatim. LLM’s use training data to construct a predictive model of which tokens follow each other. You probably could get an LLM to use samples deliberately if you wanted to, but this isn’t how they typically work.
Regardless, at that point you’re just evaluating the claim of copyright infringement based on the nature of the work itself, which is exactly what I’m advocating, versus presuming that all LLM output is necessarily copyright infringement if any copyrighted material was used in training.
exactly how the new work is created is important when it comes to derivative works.
does it use a copy of the original work to create it, or a vague idea/memory of the original work’s composition?
when i make music it’s usually vague memories. i’d argue that LLMs have an encoded representation of the original work in their weights (along with all the other stuff).
but that’s the legal grey area bit. is the “mush” of model weights an encoded representation of works, or vague memories?
not sure how i feel about being reduced down to that as a human being.
Besides, “black box” just means that your internal mental life and cognitive mechanism is opaque to me. It’s not like I’m calling you a p-zombie.
The other is a person learning from a copyrighted textbook in the legally protected manner, and whom and use the textbook was written for.
"Can you elaborate on how it's not comparable?"
The process of individual people interacting with their culture is a vastly different process than that used to train large language models. In what ways to you think these processes have anything in common?
"It seems obvious to me that it is -- they both learn and then create -- so what's the difference?"
This doesn't seem obvious to me (obviously)! Maybe you can argue that an LLM "learns" during training, but that ceases once training is complete. For sure, there are work-arounds that meet certain goals (RAG, fine-tuning); maybe your already vague definition of "learning" could be stretched to include these? Still, comparing this to how people learn is pretty far-fetched. AFAICT, there's no literature supporting the view that there's any commonality here; if you have some I would be very interested to read it. :-)
Do they both create? I suspect not; an LLM is parroting back data from it's training set. We've seen many studies showing that tested LLMs perform poorly on novel problem sets. This article was posted just this week:
https://news.ycombinator.com/item?id=42565606
The court is still out on the copyright issue, for the perspective of US law we'll have to wait on this one. Still, it's clear that an LLM can't "create" in any meaningful way.
And so on and so forth. How is hiring an employee at all similar to subscribing to an OpenAI ChatGPT plan? Wacky indeed!
But if they're learning from the same kinds of materials, and producing the same kind of output, then obviously the comparison can be made. And your idea that LLM's don't create seems obviously false.
So I have to conclude the two seem comparable, and someone would have to show why different legal principles around copyright ought to apply, when it's a simple question of input/output. Why should it matter if it's a human or algorithm doing the processing, from a copyright perspective? Nothing "wacky" about the question at all.
Strangely like the situation itself.
The question is just looked to how can we guarantee a model is influenced rather than memorising an input?
And then is a human who is influenced simply relying on a faulty or less than perfect memory?
Although with the 'good new everyone, we built the torment nexus' trajectory of AI my guess is at this point AI companies would just incorporate actual human brains instead of digital storage if that was the requirement.