[lsy]

Fixing "theoretical" nondeterminism for a totally closed individual input-output pair doesn't solve the two "practical" nondeterminism problems, where the exact same input gives different results given different preceding context, and where a slightly transformed input doesn't give a correctly transformed result.

Until those are addressed, closed-system nondeterminism doesn't really help except in cases where a lookup table would do just as well. You can't use "correct" unit tests or evaluation sets to prove anything about inputs you haven't tested.

[kazinator]

There is no such thing as "exactly the same input, but with different preceding context". The preceding context is input!

If you were to obtain exactly the same output for a given input prompt, regardless of context, then that would mean that the context is being ignored, which is indistinguishable from the session not maintaining any context such that each prompt is in a brand new empty context.

Now what some people want is requirements like:

- The different wording of a prompt with exactly the same meaning should not change anything in the output; e.g. whether you say "What is the capital of France" or "What is France's capital" the answer should be verbatim identical.

- Prior context should not change responses in ways that don't have any interaction with the context. For instance, a prompt is given "what is 2 + 2", then the answer should always be the same, except if the context instructs the LLM that 2 + 2 is to be five.

These kinds of requirements betray a misunderstanding of what these LLMs are.

[Zacharias030]

While I get that this is how LLMs work, I think you should think backwards from the user / from what AI as a field is aiming for and recognize that the „naive“ way of the parent to ask for reliable responses no matter what the „context“ is, is exactly what a good AI system should offer.

„The context is the input“ betrays a misunderstanding of what (artificial) intelligence systems are aiming for.

[Helmut10001]

Then we need something else. This is not how LLMs work. They are simple statistical predictors, now universal anwsering machines.

[Zacharias030]

I agree mostly. They are all that you say, but if you think about the conditional distribution that you are learning, there is nothing preventing us in principle from mapping different contexts to the same responses. It is rather a practical limitation that we don’t have sufficient tools of shaping these distributions very soundly. All we can do is throw data at them and hope that they generalize to similar contexts.

We have observed situations where agentic LLM traces on verifiable problems with deterministic (greedy) decoding lead to either completely correct or completely wrong solutions depending on the minutes on the clock which are printed as coincidental output of some tool that the LLM used.

I think there may be some mild fixes to current models available , for example it is worrying that the attention mechanism can never fully disregard any token in the input, because the softmax will always assign a > 0 weight everywhere (and the NN has no way of setting a logit to -infinity). This directly causes that it is extremely difficult for the LLM to fully ignore any part of the context reliably.

However Yann LeCun actually offers some persuasive arguments that autoregressive decoding has some limitations and we may need something better.

[virgilp]

> They are simple statistical predictors, now universal anwsering machines.

I see this a lot. I kinda' doubt the "simple" part, but even beyond that, is there any evidence that statistical predictor can't be a universal answering machine? I think there's plenty of evidence that our thinking is at least partially a statistical predictor (e.g. when you see a black sheep you don't think "at least one side of this sheep is black", you fully expect it to be black on both sides)

I'm not saying that LLMs _are_ universal answering machines. I'm wondering why people question that they are/they can become one, based on the argument that "fundamentally they are statistical predictors". So they are. So what?

[enormousness]

Does your definition of "universal answering machine" include the answers being correct?

If it does, statistical predictors can't help you because they're not always correct or even meaningful (correlation does not imply causation).

If it doesn't then, by all means, enjoy your infinite monkeys

[Dylan16807]

> These kinds of requirements betray a misunderstanding of what these LLMs are.

They do not. Refusing to bend your requirements to a system that can't satisfy them is not evidence of misunderstanding the system.

And if you tack on "with X 9s of reliability" then it is something LLMs can do. And in the real world every system has a reliability factor like that.

[stubbornleaf]

Sure. But the context always starts with the first input, right? And how can you guarantee—or why should you guarantee—that the reply to the first input will always be the same? And if that’s not the case, how can we ensure the preceding context remains consistent?

[kjkjadksj]

If an input along with the context generated some random seed or hash this would certainly be possible. Just paste your seed over to your coworker, they supply it to the model and it contains all contextual information.

[skybrian]

I wonder if there's a way to use an LLM to rewrite the prompt, standardizing the wording when two prompts mean the same thing?

[kazinator]

It's going to backfire. In real scenarios (not regression testing) users don't want to see the exact same thing twice out of the LLM in the same session in spite of trying to refine the result with more context.

There are going to be false positives: text that is subtly different from a previous response is misidentified as a duplicate such that the previous response is substituted for it, frustrating the user.

[skybrian]

Google search rewrites misspelled search queries and also lets you override it if that's not what you want. Maybe something similar would work?

[d4mi3n]

Not an expert, but I've been told RAG in combination with a database of facts is one way to get more consistency here. Using one of the previous examples, you might have a knowledge store (usually a vector database of some kind) that contains a mapping of countries to capitols and the LLM would query it whenever it had to come up with an answer rather than relying on whatever was baked into the base model.

[taneq]

Deterministically, you mean? ;)

[tonyhart7]

oh so you want it to be thinking???? now we talking

[raincole]

> where the exact same input gives different results given different preceding context

Why and how is this a problem?

If 'preceding context' doesn't cause different results, it means you can simply discard the context. Why do I want that? It's not how I expect a tool to work (I expect vim responds differently to my input after I switch to the insert mode). It's absolutely not how I expect intelligence to work either. It sounds like the most extreme form of confirmation bias.

[qcnguy]

When the context is auto-generated and may include irrelevant data.

This is a common AI benchmark and has been for years before GPT-2 even existed. LLMs need to not get distracted by irrelevant facts and there are tests that measure this. It's the motivation for attention mechanisms, which are the breakthrough that enabled LLMs to scale up.

[edflsafoiewq]

An example is translation. I MTLed some text recently where the name of a (fictional) city was translated about a dozen different ways. Sometimes you'd get a calque, sometimes you'd get a transliteration (including several wrong ones). Ironically "dumb" MTLs are often much more consistent about this than LLMs.

[saagarjha]

This is really useful in reproducing bugs.

[brookst]

I was with you until you said it “doesn’t really help”. Did you mean “doesn’t completely solve the problem “?