This doesn’t seem like a great way to reason about the predictions.
For something like this, saying “There is no evidence showing it” is a good enough refutation.
Counterpointing that “Well, there could be a lot of this going on, but it is in secret.” - that could be a justification for any kooky theory out there. Bigfoot, UFOs, ghosts. Maybe AI has already replaced all of us and we’re Cylons. Something we couldn’t know.
The predictions are specific enough that they are falsifiable, so they should stand or fall based on the clear material evidence supporting or contradicting them.
LLMs are completely deterministic. Their fundamental output is a vector representing a probability distribution of the next token given the model weights and context. Given the same inputs an identical output vector will be produced 100% of the time.
This fact is relied upon by for example https://bellard.org/ts_zip/ a lossless compression system that would not work if LLMs were nondeterministic.
In practice most LLM systems use this distribution (along with a “temperature” multiplier) to make a weighted random choice among the tokens, giving the illusion of nondeterminism. But there’s no fundamental reason you couldn’t for example always choose the most likely token, yielding totally deterministic output.
This is an excellent and accessible series going over how transformer systems work if you want to learn more. https://youtu.be/wjZofJX0v4M
>In practice most LLM systems use this distribution (along with a “temperature” multiplier) to make a weighted random choice among the tokens
In other words, LLMs are not deterministic in just about any real setting. What you said there only compounds with MoE architectures, variable test-time compute allocation, and o3-like sampling.
i've heard it actually depends on the model / hosting architecture. some are not deterministic at the numeric level because there is so much floating point math going on in distributed fashion across gpus, with unpredictable rounding/syncing across machines
The LLM's output is chaotic relative to the input, but it's deterministic right? Same settings, same model, same input, .. same output? Where does the chain get broken here?
Depends on what you mean specifically by the output. The actual neural network will produce deterministic outputs that could be interpreted as probability values for various tokens. But the interface you'll commonly see used in front of these models will then non-deterministiclaly choose a single next token to output based on those probabilities. Then, this single randomly chosen output is fed back into the network to produce another token, and this process repeats.
I would ultimately call the result non-deterministic. You could make it deterministic relatively easily by having a deterministic process for choosing a single token from all of the outputs of the NN (say, always pick the one with the highest weight, and if there are multiple with the same weight, pick the first one in token index order), but no one normally does this, because the results aren't that great per my understanding.
You can have the best of both worlds with something like weighted_selection( output, hash( output ) ) using the hash as the PRNG seed. (If you're paranoid about statistical issues due to identical outputs (extremely unlikely) then add a nonce to the hash.)
The only reason LLMs are stochastic instead of deterministic is a random number generator. There is nothing inherently non-deterministic about LLM algorithms unless you turn up the "temperature" of selecting the next word. The fact that determinism can be changed by turning a knob is clear evidence that they are closer to a database or search engine than a human.
I think that's more adjusting the parameters of the built-in denoising and feature detection circuits of the inherently noisy analog computer that is the brain.
Great points. I think much of the pessimism is based on fear of inadequacy. Also the fact that these things bring up truly base-level epistemological quandaries that question human perception and reality fundamentally. Average joe doesnt want to think about how we dont know if consciousness is a real thing, let alone determine if the robot is.
We are going through a societal change. There will always be the people who reject AI no matter the capabilities. I'm at the point where if ANYTHING tells me that it's conscious... I just have to believe them and act accordingly to my own morals
It's really funny how most anecdotes and comments about the utility and value of interacting with LLM's can be applied to anecdotes and comments about human beings themselves.
Majority of people havent realized yet that consciousness is assumed by our society, and that we, in fact, don't know what it is or if we have it. Let alone prescribing another entity with it.
