Long term, if no one is given credit for their research, either the creators will start to wall off their content or not create at all. Both options would be sad.
A humane attribution comment from the AI could go a long way - "I think I read something about this <topic X> in the NYTimes <link> on January 3rd, 2021."
It appears that without attribution, long term, nothing moves forward.
AI loses access to the latest findings from humanity. And so does the public.
E.g. "Japan's App Store antitrust case"
https://www.perplexity.ai/search/Japans-App-Store-GJNTsIOVSy...
Would it be more rigorous for AI to cite its sources? Sure, but the same could be said for humans too. Wikipedia editors, scholars, and scientists all still struggle with proper citations. NYT itself has been caught plagiarizing[1].
But that doesn't really solve the underlying issue here: That our copyright laws and monetization models predate the Internet and the ease of sharing/paywall bypass/piracy. The models that made sense when publishing was difficult and required capital-intensive presses don't necessarily make sense in the copy and paste world of today. Whether it's journalists or academics fighting over scraps just for first authorship (while some random web dev makes 3x more money on ad tracking), it's just not a long-term sustainable way to run an information economy.
I'd also argue that attribution isn't really that important to most people to begin with. Stuff, real and fake, gets shared on social media all the time with limited fact-checking (for better or worse). In general, people don't speak in a rigorous scholarly way. And people are often wrong, with faulty memories, or even incentivized falsehoods. Our primate brains aren't constantly in fact-checking mode and we respond better to emotional, plot-driven narratives than cold statistics. There are some intellectuals who really care deeply about attributions, but most humans won't.
Taken the above into consideration:
1) Useful AI does not necessarily require attribution
2) AI piracy is just a continuation of decades of digital piracy, and the solutions that didn't work in the 1990s and 2000s still won't work against AI
3) We need some better way to fund human creativity, especially as it gets more and more commoditized
4) This is going to happen with or without us. Cat's outta the bag.
I don't think using old IP law to hold us back is really going to solve anything in the long term. Yes, it'd be classy of OpenAI to pay everyone it sourced from, but long term that doesn't matter. Creativity has always been shared and copied and imitated and stolen, the only question is whether the creators get compensated (or even enriched) in the meantime. Sometimes yes, sometimes no, but it happens regardless. There'll always be noncommercial posts by the billions of people who don't care if AI, or a search engine, or Twitter, or whoever, profits off them.
If we get anywhere remotely close to AGI, a lot of this won't matter. Our entire economic and legal systems will have to be redone. Maybe we can finally get rid of the capitalist and lawyer classes. Or they'll probably just further enslave the rest of us with the help of their robo-bros, giving AI more rights than poor people.
But either way, this is way bigger than the economics of 19th-century newspapers...
[1] https://en.wikipedia.org/wiki/Jayson_Blair#Plagiarism_and_fa...
which is really just a very, very common story with ai problems, be it sources/citations/licenses/usage tracking/etc., it's all just 'too complex if not impossible to solve', which just seems like a facade for intentionally ignoring those problems for benefit at this point. those problems definitely exist, why not try to solve them? because well...actually trying to solve them would entail having to use data properly and pay creators, and that'd just cut into bottom line. the point is free data use without having to pay, so why would they try to ruin that for themselves?
"Here's how I would cure melanoma!" followed by your detailed findings. Zero mention of you.
F-that. Attribution, as best they can, is the least OpenAI can do as a service to humanity. It's a nod to all content creators that they have built their business off of.
Claiming knowledge without even acknowledging potential sources is gross. Solve it OpenAI.
LLM training sees these documents without context; it doesn’t know where they came from, and any such attribution would become part of the thing it’s trying to mimic.
It’s still largely an unsolved problem.
ChatGPT Browse and Bing and Google Bard implement the same pattern.
RAG does allow for some citation, but it doesn't help with the larger problem of not being able to cite for answers provided by the unassisted language model.
Not all of them will have the capability to cite a source, and plenty of them won't have it make sense to cite a source.
Eg. Suppose I train a regression that guesses how many words will be in a book.
Which book do I cite when I do an inference? All of them?
Though the other way to do it is to clearly document the training data as a whole, even if you can't cite a specific entry in it for a particular bit of generated output. It should get useless quickly though as you'd eventually have one big citation -- "The Internet"
For complex subjects, I'm sure the citation page would be large, and a count would be displayed demonstrating the depth of the subject[3].
This is how Google did it with search results in the early days[1]. Most probable to least probable, in terms of the relevancy of the page. With a count of all possible results [2].
The same attempt should be made for citations.
The issue of replicating a style is probably more difficult.
human analogies are cute, but they're completely irrelevant. it doesn't change that it's specifically about computers, and doesn't change or excuse how computers work.
But if it's possible for the neural net to memorize passages of text then surely it could also memorize where it got those passages of text from. Perhaps not with today's exact models and technology, but if it was a requirement then someone would figure out a way to do it.
Figure this out and you get to choose which AI lab you want to make seven figures at. It's a really difficult problem.
https://dspace.mit.edu/handle/1721.1/153216
As it should be.
[1] http://web.archive.org/web/20120608192927/http://www.google....
[2] https://steemit.com/online/@jaroli/how-google-search-result-...
[3] https://www.smashingmagazine.com/2009/09/search-results-desi...
[4] Next page
:)
The knowledge gets distorted, blended, and reinterpreted a million ways by the time it's given as output.
And the metadata (metaknowledge?) would be larger than the knowledge itself. The AI learnt every single concept it knows by reading online; including the structure of grammar, rules of logic, the meaning of words, how they relate to one another. You simply couldn't cite it all.
OpenAI doesn't just get to steal work and then say "sorry, not possible" and shrug it off.
The NYTimes should be suing.
To use Andrew Ng's example, you have build a multi-dimensional arrow representing "king". You compare it to the arrow for "queen" and you see that it's almost identical, except it points in the opposite direction in the gender dimension. Compare it to "man" and you see that "king" and "man" have some things in common, but "man" is a broader term.
That's getting really close to understanding as far as I'm concerned; especially if you have a large number of such arrows. It's statistical in a literal sense, but it's more like the computer used statistics to work out the meaning of each word by a process of elimination and now actually understands it.
Yes, we all agree that it's better if they do remember and mention their sources, but we don't sue them for failing to do so.
Thing is though, if you look at the prompts they used to elicit the material, the prompt was already citing the NYTimes and its articles by name.
1. If you run different software (LLM), install different hardware (GPU/TPU), and use it differently (natural language), to the point that in many ways it's a different kind of machine; does it actually surprise you that it works differently? There's definitely computer components in there somewhere, but they're combined in a somewhat different way. Just like you can use the same lego bricks to make either a house or a space-ship, even though it's the same bricks. For one: GPT-4 is not quite going to display a windows desktop for you (right-this-minute at least)
2. Comparing to humans is fine. Else by similar logic a robot arm is not a human arm, and thus should not be capable of gripping things and picking them up. Obviously that logic has a flaw somewhere. A more useful logic might be to compare eg. Human arm, Gorilla arm, Robot arm, they're all arms!
Because URLs are usually as long as the writing they point at?
Copyright law is a prehistoric and corrupt system that has been about protecting the profit margins of Disney and Warner Bros rather than protecting real art and science for living memory. Unless copy/paste superhero movies are your definition of art I suppose.
Unfortunately it seems like judges and the general public are so clueless as to how this technology works it might get regulated into the ground by uneducated people before it ever has a chance to take off. All so we can protect endless listicle factories. What a shame.
To help understand the complexity of an LLM consider that these models typically hold about 10,000 less parameters than the total characters in the training data. If one wants to instruct the LLM to search the web and find relevant citations it might obey this command but it will not be the source of how it formed the opinions it has in order to produce its output.
You are correct, if I were to steal something, surely I can be made to give it back to you. However, if I haven't actually stolen it, there is nothing for me to return.
By analogy, if OpenAI copied data from the NYT, they should be able to at least provide a reference. But if they don't actually have a proper copy of it, they cannot.
It seems like a very difficult engineering challenge to provide attribution for content generated by LLMs, while preserving the traits that make them more useful than a “mere” search engine.
Which is to say nothing about whether that challenge is worth taking on.
Now imagine terabytes worth of datapoints, and thousands of dimensions rather than two.
And on this subject, it seems worthwhile to note that compression has never freed anyone from copyright/piracy considerations before. If I record a movie with a cell phone at a worse quality, that doesn't change things. If a book is copied and stored in some gzipped format where I can only read a page at a time, or only read a random page at a time, I don't think that's suddenly fair-use.
Not saying these things are exactly the same as what LLMs do, but it's worth some thought, because how are we going to make consistent rules that apply in one case but not the other?
https://docs.github.com/en/copilot/configuring-github-copilo...
Given how cheap text search is compared with LLM inference, and that GitHub reuses the same infrastructure for its code search, I doubt it adds more than 1% to the total cost.
It doesn't have to be perfect to be helpful, and even something that is very imperfect would at least send the signal that model-owners give a shit about attribution in general.
Given a specific output, it might be hard to say which sections of the very large weighted network were tickled during the output, and what inputs were used to build that section of the network. But this level of "citation resolution" is not always what people are necessarily interested in. If an LLM is giving medical advice, I might want to at least know whether it's reading medical journals or facebook posts. If it's political advice/summary/synthesis, it might be relevant to know how much it's been reading Marx vs Lenin or whatever. Pin-pointing original paragraphs as sources would be great, but for most models it's not like there's anything that's very clear about the input datasets.
EDIT: Building on this a bit, a lot of people are really worried about AI "poisoning the well" such that they are retraining on content generated by other AIs so that algorithmic feeds can trash the next-gen internet even worse than the current one. This shows that attribution-sourcing even at the basic level of "only human generated content is used in this model" can be useful and confidence-inspiring.
Would you keep publishing articles if five people immediately stole the content and put it up on their site, claiming ownership of your research? Doubtful.
Even if LLMs can't cite their influences with current technology, that can't be a free pass to continue things this way. Of course all data brokers resist efforts along the lines of data-lineage for themselves and they want to require it from others. Besides copyright, it's common for datasets to have all kinds of other legal encumbrances like "after paying for this dataset, you can do anything you want with it, excepting JOINs with this other dataset". Lineage is expensive and difficult but not impossible. Statements like "we're not doing data-lineage and wish we didn't have to" are always more about business operations and desired profit margins than technical feasibility.
It would be great if we could tell specifically how something like ChatGPT creates its output, it would be great for research, so it's not like there is no interest in it, but it's just not an easy thing to do. It's more "Where did you get your identity from?" than "What's the author of that book?". You might think "But sometimes what the machine gives CAN literally be the answer to 'What is the author of that book?'" but even in those cases the answer is not restricted to the work alone, there is an entire background that makes it understand that thing is what you want.
I'm sorry, but pretty much nobody does this. There is no "And these books are how I learned to write like this" after each text. There is no "Thank you Pitagoras!" after using the theorem. Generally you want sources, yes, but for verification and as a way to signal reliability.
Specifically academics and researchers do this, yes. Pretty much nobody else.
This kind of mentality would have stopped the internet from existing. After all, it has been an absolute copyright nightmare, has it not?
If that's what copyright does then we are better without it.
If someone takes my software and uses it, cool. If they credit me, cool. If they don't, oh well. I'd still code.
Not everything needs to be ego driven. As long as the cancer researcher (and the future robots working alongside them) can make a living, I really don't think it matters whether they get credit outside their niches.
I have no idea who invented the CT scanner, Xray machines, the hyperdermic needle, etc. I don't really care. It doesn't really do me any good to associate Edison with light bulbs either, especially when LEDs are so much better now. I have no idea who designs the cars I drive. I go out of my way to avoid cults of personality like Tesla.
There's 8 billion of us. We all need to make a living. We don't need to be famous.
When told it is impossible they go "Geek Harder then Nerd" like demanding it will make it happen.
If someone chooses to dedicate their life to a particular domain - they sacrifice through hard work, they make hard-earned breakthroughs, then they get to dictate how their work will be utilized.
Sure, you can give it away. Your choice. Be anonymous. Your choice.
But you don't get to decide for them.
And their work certainly doesn't deserve to be stolen by an inhumane, non-acknowledging machine.
Credit in academia is more the exception to the rule, and it's that cutthroat industry that needs a better, more cooperative system.
If machines achieve sentience, does this still hold? Like, we have to license material for our sentient AI to learn from? They can't just watch a movie or read a book like a normal human could without having the ability to more easily have that material influence new derived works (unlike say Eragon, which is shamelessly Star Wars/Harry Potter/LOTR with dragons).
It will be fun to trip through these questions over the next 20 years.
These types of arguments miss the mark entirely imho. First and foremost, not every instance of copyrighted creation involves a giant corporation. Second, what you are arguing against is the unfair leverage corporations have when negotiating a deal with a rising artist.
I have no idea who invented the CT scanner, Xray machines, the hyperdermic needle, etc. I don't really care.
Maybe you should care because those things didn’t fall out do the sky and someone sure as shit got paid to develop and build those things. You copy and pasted code is worth less, a CT scanner isn’t.
If we make a machine that is capable of being as creative as humans and train it to coexist in that ecosystem then it would be fine. But that is a very unlikely case, it is much easier to make a dumb bot that plagiarizes content than to make something as creative as a human.
I disagree that our own creativity doesn't work that way: nothing is very original, our current art is based on 100k years of building up from when cave man would scrawl simple art into the stone (which they copied from nature). We are built for plagiarism, and only gross plagiarism is seen as immoral. Or perhaps, we generalize over several different sources, diluting plagiarism with abstraction?
We are still in the early days of this tech, we will be having very different conversations about it even as soon as 5 years later.
LLMs have, to my knowledge, made zero significant novel scientific discoveries. Much like crypto, they're a failure of technology to meaningfully move humanity forward; their only accomplishment is to parrot and remix information they've been trained on, which does have some interesting applications that have made Microsoft billions of dollars over the past 12 months, but let's drop the whole "they're going to save humanity and must be protected at any cost" charade. They're not AGI, and because no one has even a mote of dust of a clue as to what it will take to make AGI, its not remotely tenable to assert that they're even a stepping stone toward it.
But even if it did an exact match search is not enough here. What if you take the source code and rename all variables and functions? The filter wouldn't trigger, but it'd still be copyright infringement (whether a human or a machine does that).
For such a filter to be effective it'd at least have to build a canonical representation of the program's AST and then check for similarities with existing programs. Doing that at scale would be challenging.
Wouldn't it be better to: * Either not include copyrighted content in the training material in the first place * Explicitly tag the training material with license and origin infornation, such that the final output can produce a proof of what training material was relevant for producing that output and don't mix differently licensed content.
The model is fuzzy, it's the learning part, it'll never follow the rules to the letter the same as humans fuck up all the time.
But a model trained to be literate and parse meaning could be provided with the hard data via a vector DB or similar, it can cite sources from there or as it finds them via the internet and tbf this is how they should've trained the model.
But in order to become literate, it needs to read...and us humans reuse phrases etc we've picked up all the time "as easy as pie" oops, copyright.
Anything like word association games are basically the same exercise, but with humans and hell, I bet I could play a word association game with an LLM, too.
Having a magical ring in my book after I've read lord of the rings, is that copyright?
I wonder if there's any possibility to train the model on a wide variety of sources, only for language function purposes, then as you say give it a separate knowledge vector.
But I still haven't seen a real example of it spitting out a book verbatim. You know where I think it got chunks of "copyright" text from GRRM's books?
Wikipedia. And https://gameofthrones.fandom.com/wiki/Wiki_of_Westeros, https://awoiaf.westeros.org/index.php/Main_Page, https://data.world/datasets/game-of-thrones all the god dammed wikis, databases etc based on his work, of which there are many, and of which most quote sections or whole passages of the books.
Someone prove to me that GPT can reproduce enough text verbatim that it makes it clear that it was trained on the original text first hand basis, rather than second hand from other sources.
Yeah, good luck embedding citations into that. Everyone here saying it's easy needs to go earn their 7 figure comp at an AI company instead of wasting their time educating us dummies.