[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