Comment by kurikuri | Hacker News Reader

Comment by kurikuri | Hacker News Reader

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So, what ‘algorithms’ are you talking about? The randomness comes from the input value (the random seed). Once you give it a random seed, a special number generator (PRNG) makes a sequence from that seed. When the LLM needs to ‘flip a coin,’ it just consumes a value from the PRNG’s output sequence.

Think of each new ‘interaction’ with the LLM as having two things that can change: the context and the PRNG state. We can also think of the PRNG state as having two things: the random seed (which makes the output sequence), and the index of the last consumed random value from the PRNG. If the context, random seed, and index are the same, then the LLM will always give the same answer. Just to be clear, the only ‘randomness’ in these state values comes from the random seed itself.

The LLM doesn’t make any randomness, it needs randomness as an input (hyper)parameter.

The raw output of a transformer model is a list of logits, confidence scores for each token in its vocabulary. It's only deterministic in this sense (same input = same scores). But it can easily assign equal scores to 1 and 0 and zero to other tokens, and you'll have to sample it randomly to produce the result. Whether you consider it external or internal doesn't matter, transformers are inherently probabilistic by design. Randomness is all they produce. And typically they aren't trained with the case of temperature 0 and greedy sampling in mind.

> But it can easily assign equal scores to 1 and 0 and zero to other tokens, and you’ll have to sample it randomly to produce the result. Whether you consider it external or internal doesn’t matter, transformers are inherently probabilistic by design.

The transformer is operating on the probability functions in a fully deterministic fashion, you might be missing the forest for the trees here. In your hypothetical, the transformer does not have a non-deterministic way of selecting the 1 or 0 token, so it will rely on a noise source which can. It does not produce any randomness at all.

It's one way to look at it, but consider that you need the noise source in case 1 and 0 are strictly equal, necessarily. You can't tell which one is the answer until you decided randomly.

Right, so the LLM needs some randomness to make that decision. The LLM performs a series of deterministic operations until it needs the randomness to make this decisions, there is no randomness within the LLM itself.

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