better mental model: it's a lossy compression of human knowledge that can decompress and recombine in novel (sometimes useful, sometimes sloppy) ways.
classical search simply retrieves, llms can synthesize as well.
classical search simply retrieves, llms can synthesize as well.
Point being, in broad enough scope, search and compression and learning are the same thing. Learning can be phrased as efficient compression of input knowledge. Compression can be phrased as search through space of possible representation structures. And search through space of possible X for x such that F(x) is minimized, is a way to represent any optimization problem.
The act of compression builds up behaviors/concepts of greater and greater abstraction. Another way you could think about it is that the model learns to extract commonality, hence the compression. What this means is because it is learning higher level abstractions AND the relationships between these higher level abstractions, it can ABSOLUTELY learn to infer or apply things way outside their training distribution.
that interpolation is where synthesis happens. whether it is coherent or not depends.
There is also a RLHF training step on top of that
nathan lambert (who wrote the RLHF book @ https://rlhfbook.com/ ) describes this as the "elicitation theory of post training", the idea is that RLHF is extracting and reshaping what's already latent in the base model, not adding new knowledge. as he puts it: when you use preferences to change model behavior "it doesn't mean that the model believes these things. it's just trained to prioritize these things."
so like when you RLHF a model to not give virus production info, you're not necessarily erasing those weights, the theory is that you're just making it harder for that information to surface. the knowledge is still in the compression, RLHF just changes what gets prioritized during decompression.
1. Conceptual Distillation - Proven by research work that we can find weights that capture/influence outputs that align with higher level concepts.
2. Conceptual Relations - The internal relationships capture how these concepts are related to each other.
This is how the model can perform acts and infer information way outside of it's training data. Because if the details map to concepts then the conceptual relations can be used to infer desirable output.
(The conceptual distillation also appears to include meta-cognitive behavior, as evidenced by Anthropic's research. Which manes sense to me, what is the most efficient way to be able to replicate irony and humor for an arbitrary subject? Compressing some spectrum of meta-cognitive behavior...)
Also, I was under the impression LLM's can replicate irony and humor simply because that text has specific stylistic properties, and they've been trained on it.
Other than that the model should be able to learn in context for most things based on the component concepts. Similar to how you learn in context.
There aren't a lot of limits in my experience. Rarely you'll hit patterns that are too powerful where it is hard for context to alter behavior, but those are pretty rare.
The models can mix and match concepts quite deeply. Certainly, if it is a completely novel concept that can't be described by a union or subtraction between similar concepts, than the model probably wouldn't handle it. In practice, a completely isolated concept is pretty rare.
Not quite autocomplete but not intelligence either.
However, such novel insights are not novel due to the LLM, but due to you.
The "novel" insights are either novel only to you, because they belong to something that you have not studied before, or they are novel ideas that were generated by yourself as a consequence of your attempts to explain what you want to the LLM.
It is very frequent for someone to be led to novel insights about something that he/she believed to already understand well, only after trying to explain it to another ignorant human, when one may discover that the previous supposed understanding was actually incorrect or incomplete.
I talk with LLMs for hours out of the day, every single day. I'm deeply familiar with their strengths and shortcomings on both a technical and intuitive level. I push them to their limits and have definitely witnessed novel output. The question remains, just how novel can this output be? Synthesis is a valid way to produce novel data.
And beyond that, we are teaching these models general problem-solving skills through RL, and it's not absurd to consider the possibility that a good enough training regimen cannot impart deduction/induction skills into a model that are powerful enough to produce novel information even via means other than direct synthesis of existing information. Especially when given affordances such as the ability to take notes and browse the web.
I’m quite curious what these novel outputs are. I imagine the entire world would like to know of an LLM producing completely, never-before-created outputs which no human has ever thought before.
Here is where I get completely hung up. Take 2+2. An LLM has never had 2 groups of two items and reached the enlightenment of 2+2=4
It only knows that because it was told that. If enough people start putting 2+2=3 on the internet who knows what the LLM will spit out. There was that example a ways back where an LLM would happily suggest all humans should eat 1 rock a day. Amusingly, even _that_ wasn’t a novel idea for the LLM, it simply regurgitated what it scraped from a website about humans eating rocks. Which leads to the crux: how much patently false information have LLMs scraped that is completely incorrect?
As an example: https://arxiv.org/abs/2301.05217
It's not hard to imagine that a sufficiently developed manifold could theoretically allow LLMs to interpolate or even extrapolate information that was missing from the training data, but is logically or experimentally valid.
Circuits are the basis for features. There is still a ton of open research on this subject. I don't care what you care about, the research is still being done and it's not a new concept.
I argue absolutely not. That would be a fascinating experiment.
Hell, train it on every 2-number addition combination of m+n where m and n can be any number between 1-100 (or 0-100 would be better) BUT 2, and have it figure out what 2+2 is.
I would probably change my opinion about “circuits”, which by the way really stretches the idea of a circuit. The “circuit” is just the statistically most likely series of tokens that you’re drawing pretend lines between. Sure, technically connect-the-dots is a circuit, but not in the way you’re implying, or that paper.
What? Of course not? Could you? Do you understand just how much work has gone into proving that 1 + 1 = 2? Centuries upon centuries of work, reformulating all of mathematics several times in the process.
> Hell, train it on every 2-number addition combination of m+n where m and n can be any number between 1-100 (or 0-100 would be better) BUT 2, and have it figure out what 2+2 is.
If you read the paper I linked, it shows how a constrained modular addition is grokked by the model. Give it a read.
> The “circuit” is just the statistically most likely series of tokens that you’re drawing pretend lines between.
That is not what ML researchers mean when they say circuit, no. Circuits are features within the weights. It's understandable that you'd be confused if you do not have the right prior knowledge. Your inquiries are good, but they should stop as inquiries.
If you wish to push them to claims, you first need to understand the space better, understand what modern research does and doesn't show, and turn your hypotheses into testable experiments, collect and publish the results. Or wait for someone else to do it. But the scientific community doesn't accept unfounded conjecture, especially from someone who is not caught up with the literature.
Turns out counting 2 sets of two objects (1… 2… 3… 4…) isn’t actually hard to do if you teach the kid how to count to 10 and that 1+1=2
I guess when we get to toddler stage of LLMs I’ll be more interested.
And does your child's understanding of mathematics scale? I'm sure your 4-year-old would fail at harder arithmetic. Can they also tell me why 1+1=2? Like actually why we believe that? LLMs can do that. Modern LLMs are actually insanely good at not just basic algebra, but abstract, symbolic mathematics.
You're comparing apples and oranges, and seem to lack foundational knowledge in mathematics and computer science. It's no wonder this makes no sense to you. I was more patient about it before, but now this conversation is just getting tiresome. I'd rather spend my energy elsewhere. Take care, have a good day.
> If an LLM had only ever seen 1+1=2 and that was the only math they were ever exposed to, along with the numbers 0-10, could an LLM figure out that 2+2=4?
Unless you locked your kid in a room since birth with just this information, it is not the same kind of set up is it?
Lack of knowledge is one thing, arrogance is another.
On the other hand, if the problem you have to solve has never been solved before at a quality satisfactory for your purpose, then it is futile to ask an AI coding assistant to provide a solution, because it is pretty certain that the proposed solution will be unacceptable (unless the AI succeeds to duplicate the performance of a monkey that would type a Shakespearean text by typing randomly).
Joking aside, I think you have too strict of a definition of novel. Unfortunately "novel" is a pretty vague word and is definitely not a binary one.
ALL models can produce "novel" data. I don't just mean ML (AI) models, but any mathematical model. The point of models is to make predictions about results that aren't in the training data. Doing interpolation between two datapoints does produce "novel" things. Thinking about the parent's comment, is "a blue tiger" novel? Probably? Are there any blue tigers in the training data? (there definitely is now thanks to K-Pop Demon Hunters) If not, then producing that fits the definition of novel. BUT I also agree that that result is not that novel. It is entirely unimpressive.
I'm saying this not because I disagree with what I believe you intend to say but because I think a major problem with these types of conversations is that many people are going to interpret you more literally and dismiss you because "it clearly produces novel things." It isn't just things being novel to the user, though that is also incredibly common and quite telling that people make such claims without also checking Google...
Speaking of that, I'm just going to leave this here... I'm still surprised this is a real and serious presentation... https://www.youtube.com/watch?v=E3Yo7PULlPs&t=616s