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What's actually happening in a LLM is many orders of magnitude more complex than a Markov chain. However, I agree that they're an amazing pedagogical tool for the basic principles of how a LLM works, even to a non-technical audience. Many people try to "explain" LLMs starting with the principles of neural networks. This rarely works well: there are some significant conceptual leaps required. However, explaining that…
This is what I've been saying to people, LLMs aren't much smarter than a Markov Chain - a lot of twitter bots and earlier chat agents were driven by them. What I do see though is the infrastructure around LLMs making a difference.
Then you're doing some combination of grossly overestimating the latter and/or underestimating the former.
A Markov Chain is a (small) language model with a context length of 1, never more, that's a definitional requirement of a Markov process.
Earlier quoted context omitted.
I've never seen a Markov chain do anything like GPT4. I'm not sure how you can say with a straight face they are basically the same.
No one has spent 100M on training Markov chains.
https://en.wikipedia.org/wiki/Markov_chain#/media/File:Marko...
Earlier quoted context omitted.
It's not just window size. It's the difference between syntax and semantics. A Markov model, by definition, works only with literal token histories. It can't participate meaningfully in a conversation unless the user happens to employ token sequences that the model has seen before (ideally multiple times.) An LLM can explain why it's not just a Markov model, but the converse isn't true. Now, if you were to add high-d…
>high-dimensional latent-space embedding to a Markov model That's what we call a hidden Markov model. >There's a school of thought that says that lossy compression doesn't just require intelligence, it is intelligence, and LLMs can be seen as an example of that equivalence. SVD is used to implement lossy compression as does JPEG encoding... these algorithms are in no way intelligent.
You'll have to take that up with people above my pay grade. It's not that simple, apparently. Call me when a Markov model can explain why it's equivalent to an LLM.
Earlier quoted context omitted.
No one has spent 100M on training Markov chains.
Google did it. Amazon did it. Plenty of others did. What do you think they were doing before recurrent neural networks?
No.
I'd believe $40 for the energy cost, $120 for very slightly increased wear on their hard drives, and $400 for one engineer's 20% time project for one week.
And that's if it was trained on Google's entire internet cache rather than, say, just a Wikipedia snapshot from 2004, which sounds like the kind of thing that Google might have set as a pre-interview coding challenge.
What's actually happening in a LLM is many orders of magnitude more complex than a Markov chain. However, I agree that they're an amazing pedagogical tool for the basic principles of how a LLM works, even to a non-technical audience. Many people try to "explain" LLMs starting with the principles of neural networks. This rarely works well: there are some significant conceptual leaps required. However, explaining that…
What makes Markov chains such a great pedagogic tool right now is that they share (approximately) the same interface, being a token predictor, and that current LLMs are much closer to the capabilities of a fantastically good Markov chain than those of an above-human intelligence.
What's actually happening in a LLM is many orders of magnitude more complex than a Markov chain. However, I agree that they're an amazing pedagogical tool for the basic principles of how a LLM works, even to a non-technical audience. Many people try to "explain" LLMs starting with the principles of neural networks. This rarely works well: there are some significant conceptual leaps required. However, explaining that…
What's actually happening in a LLM is many orders of magnitude more complex than a Markov chain. However, I agree that they're an amazing pedagogical tool for the basic principles of how a LLM works, even to a non-technical audience. Many people try to "explain" LLMs starting with the principles of neural networks. This rarely works well: there are some significant conceptual leaps required. However, explaining that…
At risk of showing my deficient understanding: that isn't actually true, is it?[1]
LLMs do much more than simply predict subsequent text, AFAICT. I think back to the earlier LLM results that wowed the world with stuff like "now I have a model where you can ask it 'king - man + woman' ... and it returns 'queen'. Trippy!"
That is pretty clearly not mere text prediction, at least not identically. Even nearly all of the computational "hard work" comes from the math of predicting subsequent letters, that whole computation is introducing a new primitive, the concept of combining, or doing math on, models to produce other models, and thereby making a statement about what words would come next in a variety of scenarios.
That primitive is not present in the Markov predictor example, at least not without positing how a Markov predictor would be similarly transformed -- which, being very ignorant on the matter, I'm not sure is possible or not, but either way, leaves out a critical construct that enables ChatGPT to e.g. find limericks that aren't preceded by the command "Write me a limerick", as in my earlier comment[1].
[1] Earlier comment on why I think it's dubious to call LLM-based products such as ChatGPT "mere LMs": https://news.ycombinator.com/item?id=35472089
What's actually happening in a LLM is many orders of magnitude more complex than a Markov chain. However, I agree that they're an amazing pedagogical tool for the basic principles of how a LLM works, even to a non-technical audience. Many people try to "explain" LLMs starting with the principles of neural networks. This rarely works well: there are some significant conceptual leaps required. However, explaining that…
Genuine question: what do you mean by many orders of magnitude more complex?
