[dlandis]

> There is no way to know what sources have been memorized vs which have made their mark by affecting other types of functions in the neural net.

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.

[wrs]

Except it doesn’t memorize text. It generates text that is statistically likely. Generating a citation that is statistically likely wouldn’t really help the problem.

[__loam]

So it's just bullshit then.

[fennecbutt]

It's literally how our meat bag brains work pretty much.

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.

[Tao3300]

Neural nets don't memorize passages of text. They train on vectorized tokens. You get a model of how language statistically works, not understanding and memory.

[tsimionescu]

The model weights clearly encode certain full passages of text, otherwise it would be virtually impossible for the network to produce verbatim copies of text. The format is something very vaguely like "the most likely token after "call" is "me"; the most likely token after "call me" is "Ishmael". It's ultimately a kind of lossy statistical compression scheme at some level.

[photonthug]

> It's ultimately a kind of lossy statistical compression scheme at some level.

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?

[fennecbutt]

Is it still compression if I read Tolkien and reference similar or exact concepts when writing my own works?

Having a magical ring in my book after I've read lord of the rings, is that copyright?

[tsimionescu]

Generally, no, copyright deals with exact expression, not concepts. However, that can include the structure of a work, so if you wrote a book about little people who form a band together with humans and fairies and a mage to destroy a ring of power created by an ancient evil, where the start in their nice home but it gets attacked by the evil lord's knights [...] you may be breaking Tolkien's copyright.

[seanmcdirmid]

If you watch a bunch of movies then go on to make your own movie based on influence from these movies, you are protected even if you have mentally compressed them into your own movie. At some point, you can learn, be influenced and be inspired from copyrighted material (not copyright infringement), and at some point you are just making a poor copy of the material (definitely copyright infringement). LLMs are probably still at the latter case than the former, but eventually AI will reach the former case.

[photonthug]

There's no obvious need to hold people / AI to same standards here, yet, even if compression in mental-models is exactly analogous to compression in machine-models. I guess we decided already that corporations are already "like" persons legally, but the jury is still out on AIs. Perhaps people should be allowed more leeway to make possibly-questionable derivative works, because they have lives to live, and genuine if misguided creative urges, and bills to pay, etc. Obviously it's quite difficult to try and answer the exact point at which synthesis & summary cross a line to become "original content". But it seems to me that, if anything, machines should be held to higher standard than people.

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.

[seanmcdirmid]

> But it seems to me that, if anything, machines should be held to higher standard than people.

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.

[tremon]

But that's not what ChatGPT is doing, or is it? ChatGPT watches and records a bunch of movies, then stitches together its own movie using scenes and frames from the movies it recorded. AI will never reach the former case until it learns to operate a camera.

[seanmcdirmid]

How do you not know this isn’t what we are doing in some more advanced form? Anyways, the comparisons will become more apt as the tech advances.

[FredPret]

You can encode understanding in a vector.

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.