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Comment by matusp | Hacker News Reader

What do you mean? The states are fully observable (current array of tokens), and using an LLM we calculate the probabilities of moving between them. What is not MC about this?

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I suggest getting familiar with or brushing up on the differences between a Markov Chain and a Markov Model. The former is a substantial restriction of the latter. The classic by Kemeny and Snell is a good readable reference.

MC have constant and finite context length, their state is the most recent k tuple of emitted alphabets and transition probabilities are invariant (to time and tokens emitted)

LLMs definitely also have finite context length. And if we consider padding, it is also constant. The k is huge compared to most Markov chains used historically, but it doesn't make it less finite.

That's not correct. Even a toy like an exponential weighted moving averaging produces unbounded context (of diminishing influence).

What do you mean? I can only input k tokens into my LLM to calculate the probs. That is the definition of my state. In the exact way that N-gram LMs use N tokens, but instead of using ML models, they calculate the probabilities based on observed frequencies. There is no unbounded context anywhere.

That's different.

You can certainly feed k-grams one at a time to, estimate the the probability distribution over next token and use that to simulate a Markov Chain and reinitialize the LLM (drop context). In this process the LLM is just a look up table to simulate your MC.

But an LLM on its own doesn't drop context to generate, it's transition probabilities change depending on the tokens.

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