4 ms·
Fundamentally there's no way to deterministically guarantee anything about the output.
by HPsquared 6mo ago
Fundamentally there's no way to deterministically guarantee anything about the output.
- satvikpendem 6mo agoThat is "fundamentally" not true, you can use a preset seed and temperature and get a deterministic output.
- HPsquared 6mo agoI'll grant that you can guarantee the length of the output and, being a computer program, it's possible (though not always in practice) to rerun and get the same result each time, but that's not guaranteeing anything about said output.
- satvikpendem 6mo agoWhat do you want to guarantee about the output, that it follows a given structure? Unless you map out all inputs and outputs, no it's not possible, but to say that it is a fundamental property of LLMs to be non deterministic is false, which is what I was inferring you meant, perhaps that was not what you implied.
- program_whiz 6mo agoYeah I think there are two definitions of determinism people are using which is causing confusion. In a strict sense, LLMs can be deterministic meaning same input can generate same output (or as close as desired to same output). However, I think what people mean is that for slight changes to the input, it can behave in unpredictable ways (e.g. its output is not easily predicted by the user based on input alone). People mean "I told it don't do X, then it did X", which indicates a kind of randomness or non-determinism, the output isn't strictly constrained by the input in the way a reasonable person would expect.
- yunwal 6mo agoThe correct word for this IMO is "chaotic" in the mathematical sense. Determinism is a totally different thing that ought to retain it's original meaning.
- wat10000 6mo agoThey didn't say LLMs are fundamentally nondeterministic. They said there's no way to deterministically guarantee anything about the output. Consider parameterized SQL. Absent a bad bug in the implementation, you can guarantee that certain forms of parameterized SQL query cannot produce output that will perform a destructive operation on the database, no matter what the input is. That is, you can look at a bit of code and be confident that there's no Little Bobby Tables problem with it. You can't do that with an LLM. You can take measures to make it less likely to produce that sort of unwanted output, but you can't guarantee it. Determinism in input->output mapping is an unrelated concept.
- silon42 6mo agoYou can guarantee what you have test coverage for :)
- bdangubic 6mo agodepends entirely on the quality of said test coverage :)
- rightofcourse 6mo agohaha, you are not wrong, just when a dev gets a tool to automate the _boring_ parts usually tests get the first hit
- simianparrot 6mo agoA single byte change in the input changes the output. The sentence "Please do this for me" and "Please, do this for me" can lead to completely distinct output. Given this, you can't treat it as deterministic even with temp 0 and fixed seed and no memory.
- satvikpendem 6mo agoWell yeah of course changes in the input result in changes to the output, my only claim was that LLMs can be deterministic (ie to output exactly the same output each time for a given input) if set up correctly.
- idiotsecant 6mo agoYou don't think this is pedantry bordering on uselessness?
- satvikpendem 6mo agoIt's correcting a misconception that many people have regarding LLMs that they are inherently and fundamentally non-deterministic, as if they were a true random number generator, but they are closer to a pseudo random number generator in that they are deterministic with the right settings.
- deleted 6mo ago[deleted]
- WithinReason 6mo agoNo, determinism and predictability are different concepts. You can have a deterministic random number generator for example.
- albedoa 6mo agoThe comment that is being responded to describes a behavior that has nothing to do with determinism and follows it up with "Given this, you can't treat it as deterministic" lol. Someone tried to redefine a well-established term in the middle of an internet forum thread about that term. The word that has been pushed to uselessness here is "pedantry".
- yunohn 6mo agoI initially thought the same, but apparently with the inaccuracies inherent to floating-point arithmetic and various other such accuracy leakage, it’s not true! https://arxiv.org/html/2408.04667v5 https://arxiv.org/html/2408.04667v5
- layer8 6mo agoThis has nothing to do with FP inaccuracies, and your link does confirm that: “Although the use of multiple GPUs introduces some randomness (Nvidia, 2024), it can be eliminated by setting random seeds, so that AI models are deterministic given the same input. […] In order to support this line of reasoning, we ran Llama3-8b on our local GPUs without any optimizations, yielding deterministic results. This indicates that the models and GPUs themselves are not the only source of non-determinism.”
- yunohn 6mo agoI believe you've misread - the Nvidia article and your quote support my point. Only by disabling the fp optimizations, are the authors are able to stop the inaccuracies.
- layer8 6mo agoFirst, the “optimizations” are not IEEE 754 compliant. So nondeterminism with floating-point operations is not an inherent property of using floating-point arithmetics, it’s a consequence of disregarding the standard by deliberately opting in to such nondeterminism. Secondly, as I quoted the paper is explicitly making the point that there is a source of nondeterminism outside of the models and GPUs, hence ensuring that the floating-point arithmetics are deterministic doesn’t help.
- 4ndrewl 6mo agoIf you also control the model.
- zbentley 6mo agoPractically, the performance loss of making it truly repeatable (which takes parallelism reduction or coordination overhead, not just temperature and randomizer control) is unacceptable to most people.
- wat10000 6mo agoIt's also just not very useful. Why would you re-run the exact same inference a second time? This isn't like a compiler where you treat the input as the fundamental source of truth, and want identical output in order to ensure there's no tampering.
- mhitza 6mo agoIf you self-host an LLM you'll learn quickly that even batching, and caching can affect determinism. I've ran mostly self-hosted models with temp 0 and seen these deviations.
- phlakaton 6mo agoBut you cannot predict a priori what that deterministic output will be – and in a real-life situation you will not be operating in deterministic conditions.
- vrighter 6mo agodeterministic is useless if it means it will always make the same mistake it did the first time.
- WithinReason 6mo agoOf course there is, restrict decoding to allowed tokens for example
- paulryanrogers 6mo agoWhat would this look like?
- WithinReason 6mo agothe model generates probabilities for the next token, then you set the probability of not allowed tokens to 0 before sampling (deterministically or probabilistically)
- PunchyHamster 6mo agobut filtering a particular token doesn't fix it even slightly, because it's a language model and it will understand word synonyms or references.
- WithinReason 6mo agoI'm obviously talking about network output, not input.
- PunchyHamster 6mo agowhich you can affect by just telling it to use different wording... or language for that matter
- zbentley 6mo agoGood-token/bad-token overlap is near 100%. For example, try interacting with quantitative data, or program code, without using these tokens: > :(){ :|: & };: Now try running that in your shell.
- vrighter 6mo ago
- sjdv1982 6mo agoNatural language is ambiguous. If both input and output are in a formal language, then determinism is great. Otherwise, I would prefer confidence intervals.
- forlorn_mammoth 6mo agoHow do you make confidence intervals when, for example, 50 english words are their own opposite?
- sjdv1982 6mo agoI would like the AI to attach a confidence interval that the answer is "Yes" rather than "No". AlphaFold does this very well, but LLMs... not so much.