It's not a Markov Chain or a fancy text predictor. It's a ~200 layer neural network that models a vast hierarchy of concepts through language. It has emergent properties that we don't yet understand.
GPT-3.5, like its predecessor GPT-3, is not a Markov chain. GPT-3.5 is based on the GPT (Generative Pre-trained Transformer) architecture, which is a type of neural network known as a Transformer. Transformers use self-attention mechanisms to process and generate text, allowing them to capture long-range dependencies and context in the input data.
On the other hand, a Markov chain is a stochastic model that describes a sequence of possible events, where the probability of each event depends only on the state attained in the previous event. While Markov chains can be used for simple text generation, they lack the ability to capture the complex relationships and long-range dependencies that GPT-3.5 can handle.
A tennis ball in flight is a Markov chain since the state at t is a function of the state at t-1.
You have missed the point about the Attention Mechanism in GPT. That is not a Markov chain by definition.
Well I guess maybe it's true that you can turn any stochastic process into a Markov Chain by changing the state space somehow (for example the states could be sample trajectories up to some finite time T). And while this is true it may be not very insightful.
But I personally think that to understand LLMs it is much better to think of the whole context window as a state rather than the individual states. If you modelled a simple register-instruction computer as a stochatic process, would you take the states to be (address last symbol written, last symbol written)? It makes much more sense to take the whole memory as a state. Similarly a transformer operates on its memory, the context window, so that should be seen as the state. This makes it clear that seeing it as just a stochastic parrot is misleading, as its all about conditioning the distribution of the next token via prompt engineering the previous tokens. And it is nevertheless a Markov chain with this state space.
You know, I think that some people (I see on twitter, probably not you) have a wrong intuition about artificial intelligence. They see models which are fundamentally stochastic as incapable of really ever being truly intelligent. It's "just statistics" or just a "stochastic parrot" and it just learns probabilities instead of real meaning. Perhaps they think that since there is always randomness involved, you can not have the kind of deterministic thought process that we feel we have. The worst offender is then considered to be the old school Markov chain.
I obviously think this is wrong and that's why I like to emphasize that transformers are best interpreted as Markov Chains on a larger state space, and this does actually explain their computational behavior.
The Transformer architecture does not satisfy the Markov property by formal definition. ChatGPT is not a Markov chain.
However the Turing machine which is executing the algorithm does satisfy the Markov property. So you're talking about a lower level of abstraction where any computation of any algorithm is just "running on a Markov chain".
https://arxiv.org/abs/2212.10559 shows a LLM is doing gradient descent on the context window at inference time.
If it's learning relationships between concepts at runtime based on information in the context window then it seems about as useful to say it is a Markov chain as it is to say that a human is a Markov chain. Perhaps we are, but the "current state" is unmeasurably complex.
* a kind of stochastic model * a "naive" realization of that model which directly counts frequencies of N-dimensional vectors
This naive implementation is sometimes used for language modeling, e.g. for the purpose of compression. So people might think you mean that particular implementation rather than a theoretical model.
This sort of a description can be unhelpful.