this post was submitted on 06 Dec 2023
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[–] BURN@lemmy.world 6 points 11 months ago (5 children)

Most LLMs can be made to spit out training data. That’s pretty much replication in my book.

Statistical models don’t create anything. They replicate variations of their training data.

[–] Dkarma@lemmy.world 1 points 11 months ago (1 children)

Painters replicate variations of their training pieces too. You're pretending there's a difference between human inspired and training inspired and that you should get paid for that inspiration in one case just cuz "big corp"

[–] BURN@lemmy.world 3 points 11 months ago (1 children)

Because there is a difference. A computer does not learn or understand anything. Human beings can transform a concept. A LLM or other generative AI does not transform a concept at all.

[–] stephen01king@lemmy.zip -2 points 11 months ago* (last edited 11 months ago) (1 children)

So if I ask it to create a story about a cow juggling bowling balls, it was not creating an original story? Just spitting out stories it has heard of before?

Edit: missed a 'not'.

[–] BURN@lemmy.world 2 points 11 months ago* (last edited 11 months ago) (1 children)

It’s spitting out variations of the statistical results based on your input parameter. It reorganizes ideas and reorganizes the stories it has seen into something else. That’s not transforming the data by adding something new, rather just retrofitting existing data to sound like it’s creating something new

[–] stephen01king@lemmy.zip 1 points 11 months ago

retrofitting existing data to sound like it’s creating something new.

What the difference? That is basically how new human ideas are formed. Did you think you add completely new ideas everytime you transform your previous knowledge?

But since you're so confident in your claims, I'm sure it should be easy to prove the following ChatGPT output is not new and can be easily traced back to its training data:

Prompt: Create a short poem about a cow juggling bowling balls on a boat

Output: In a boat on gentle waves it sways, A cow, not grazing in greenish bays. Hooves deftly juggle, balls in flight, Bowling orbs, a whimsical sight.

Bovine artist, on the sea's embrace, Balancing spheres with tranquil grace. Ocean breeze, a playful gale, A cow's performance, a quirky tale.

[–] stephen01king@lemmy.zip 1 points 11 months ago (2 children)
[–] BURN@lemmy.world 2 points 11 months ago (1 children)
[–] stephen01king@lemmy.zip -2 points 11 months ago

Thanks for the link, I've actually seen this one. I'm just wondering how common it is since you mentioned it can be done on most LLMs.

[–] teuast@lemmy.ca 1 points 11 months ago (1 children)

...All of them? That's literally how all of them work.

[–] stephen01king@lemmy.zip 0 points 11 months ago

Then, it should be easy for you to show some examples.

[–] zwaetschgeraeuber@lemmy.world 0 points 11 months ago

when you read something and recite it, what do you do? exactly, spitting out the training data, if you trained long enough

[–] curiousaur@reddthat.com 0 points 11 months ago

Humans don't create anything. They replicate variations of their training data.

[–] theneverfox@pawb.social -2 points 11 months ago

No, statistical next word prediction was the first step, and you could get it to spit out bits of training data, but we're so far beyond that now with LLMs.

I've been doing a lot with llama derivative models that I talk with, I use them for tasks but also just bounce ideas off them or chat. They're very different when you run them with a task vs feed in a prompt and multi-turn conversation.

Mine have a very strong tendency, when asked the name of a hallucinated friend or family member to name her Luna or fluffy. It's present in the base llama2, as well as some of the fine-turned versions I'm using now.

Why? That's not training data - they're not uncommon as pet names, but there's no way they show up often referring to sapient beings (which is the context they're brought up in).

It's an artifact of some sort for sure, but that is not a statistically likely next word choice based on training data.

I could talk about this all day and it gets so much weirder, but I'll give you another story. They like to play, but their world is text, and I like to see what comes out of the models when you "yes, and" them while avoiding leading questions.

Some games they've made up... Hide and seek (they're usually in the second place you Guess), and my favorite - find the coma (and the related find the missing semicolon).

WTF even is that? It's the kind of simplistic "game" a child makes up as they experiment with moving beyond mimicry to generalizing, and the fact that it's coherent and has an appropriate answer is pretty amazing.

These LLMs aren't just statistics, there's a nascent internal model of the world that you get glimpses of if you tell it it's a person and feed its outputs back into itself. I was pretty dismissive of the "sparks of AGI" comment when it was made, but a few months of hands on interaction has totally flipped my opinion of where these are at