No. It’s not. A Markov chain has some very specific properties that are absolutely not fulfilled by GPT-3 models.

Just say “stochastic” if you want a buzzword. Stop appropriating Markov chains.

A Markov chain is a model where: (1) there is a state, (2) the probabilities of the next state depend only on the current state.

That could describe anything from a wet fart to a deterministic computer to GPT-3 to the human mind to quantum mechanics (the real one, not a simulation).

If I unplug the Internet and all USB devices, my computer is a Markov chain with at least several trillion bits of state, so 2^(several trillion) possible states. And there is one next state, which has a probability of 1, and all other possible states have a probability of 0. That's a Markov chain.

GPT models choose the next word probabilistically, with the probabilities chosen by feeding the previous N words into a neural network. That sounds like a Markov chain to me!

What properties does a gpt-3 model have that a Markov chain doesn’t? (Other than effectiveness.)

GPT-3 is conditioned on the entire input sequence as well as its own output, which is strictly NON-MARKOVIAN. In fact, the point in saying something is Markovian is exactly that: the state transition probability only depends on the current state.

"Given a prompt, provide a completion" is what a Markov chain does. GPT-3 is exactly the same, in the sense that apples and oranges both satisfy your hunger.