I think you’re anthropomorphising the tech tbh. It’s not a person or an animal, it’s a machine and cramming doesn’t work in the idea of neural networks. They’re a mathematical calculation over a vast multidimensional matrix, effectively solving a polynomial of an unimaginable order. So “cramming” as you put it doesn’t work because by definition an LLM cannot forget information because once it’s applied the calculations, it is in there forever. That information is supposed to be blended together. Overfitting is the closest thing to what you’re describing, which would be inputting similar information (training data) and performing the similar calculations throughout the network, and it would therefore exhibit poor performance should it be asked do anything different to the training.
What I’m arguing over here is language rather than a system so let’s do that and note the flaws. If we’re being intellectually honest we can agree that a flaw like reproducing large portions of a work doesn’t represent true learning and shows a reliance on the training data, i.e. it cant learn unless it has seen similar data before and certain inputs provide a chance it just parrots back the training data.
In the example (repeat book over and over), it has statistically inferred that those are all the correct words to repeat in that order based on the prompt. This isn’t akin to anything human, people can’t repeat pages of text verbatim like this and no toddler can be tricked into repeating a random page from a random book as you say. The data is there, it’s encoded and referenced when the probability is high enough. As another commenter said, language itself is a powerful tool of rules and stipulations that provide guidelines for the machine, but it isn’t crafting its own sentences, it’s using everyone else’s.
Also, calling it “tricking the AI” isn’t really intellectually honest either, as in “it was tricked into exposing it still has the data encoded”. We can state it isn’t preferred or intended behaviour (an exploit of the system) but the system, under certain conditions, exhibits reuse of the training data and the ability to replicate it almost exactly (plagiarism). Therefore it is factually wrong to state that it doesn’t keep the training data in a usable format - which was my original point. This isn’t “cramming”, this is encoding and reusing data that was not created by the machine or the programmer, this is other people’s work that it is reproducing as it’s own. It does this constantly, from reusing StackOverflow code and comments to copying tutorials on how to do things. I was showing a case where it won’t even modify the wording, but it reproduces articles and programs in their structure and their format. This isn’t originality, creativity or anything that it is marketed as. It is storing, encoding and copying information to reproduce in a slightly different format.
EDITS: Sorry for all the edits. I mildly changed what I said and added some extra points so it was a little more intelligible and didn’t make the reader go “WTF is this guy on about”. Not doing well in the written department today so this was largely gobbledegook before but hopefully it is a little clearer what I am saying.
Dammit, so my comment to the other person was a mix of a reply to this one and the last one… not having a good day for language processing, ironically.
Specifically on the dragonfly thing, I don’t think I’ll believe myself naive for writing that post or this one. Dragonflies arent very complex and only really have a few behaviours and inputs. We can accurately predict how they will fly. I brought up the dragonfly to mention the limitations of the current tech and concepts. Given the worlds computing power and research investment, the best we can do is a dragonfly for intelligence.
To be fair, Scientists don’t entirely understand neurons and ML designed neuron-data structures behave similarly to very early ideas of what brains do but its based on concepts from the 1950s. There are different segments of the brain which process different things and we sort of think we know what they all do but most of the studies AI are based on is honestly outdated neuroscience. OpenAI seem to think if they stuff enough data into this language processor it will become sentient and want an exemption from copyright law so they can be profitable rather than actually improving the tech concepts and designs.
Newer neuroscience research suggest neurons perform differently based on the brain chemicals present, they don’t all always fire at every (or even most) input and they usually present a train of thought, I.e. thoughts literally move around in the brains areas. This is all very different to current ML implementations and is frankly a good enough reason to suggest the tech has a lot of room to develop. I like the field of research and its interesting to watch it develop but they can honestly fuck off telling people they need free access to the world’s content.
TL;DR dragonflies aren’t that complex and the tech has way more room to grow. However, they have to generate revenue to keep going so they’re selling a large inference machine that relies on all of humanities content to generate the wrong answer to 2+2.