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A Markov chain satisfies the Markov property [1] in which the probability of an event is conditioned only on the previous event. Regardless of whether an LLM ev
by akprasad 4y ago
A Markov chain satisfies the Markov property [1] in which the probability of an event is conditioned only on the previous event. Regardless of whether an LLM event is a character or a word, an LLM certainly does not follow a Markov process.
[1]: https://en.wikipedia.org/wiki/Markov_property https://en.wikipedia.org/wiki/Markov_property
- inimino 4y agoThe state space of a Markov process must be fixed but is otherwise unspecified, which the LLM satisfies, so you are technically incorrect.
- kelipso 4y agoIf you consider the entire input for the language model as the state, and consider the output to be the input concatenated with the next token, then it's a markov chain. But that's only if you don't use BEAM search, which LLMs do use. If you use BEAM search with beam=4 (typical for LLMs), for example, then it does a tree search where it keeps track of the top 4 highest probability outputs, then I guess it's not a markov chain process anymore but it just uses a markov chain.