Poor comparison
Models have been improving. By induction they’ll continue until we see them stop. There is no prevailing understanding of models that lets us predict a parameter and/or training set size after which they’ll plateau. So arguing “how do we know they’ll get better” is the same as arguing “how do we know the sun will rise tomorrow”… We don’t, technically, but experience shows it’s the likely outcome.
The LLM true believers have decided that (a) hallucinations will eventually go away as these models improve, it's just a matter of time; and (b) people who complain about hallucinations are setting the bar too high and ignoring the fact that humans themselves hallucinate too, so their complaints are not to be taken seriously.
In other words, logic is not going to win this argument. I don't know what will.
What I think is actually happening is that some people innately have taken the stance that it’s impossible for an AI model to be useful if it ever hallucinates, and they probably always will hallucinate to some degree or under some conditions, ergo they will never be useful. End of story.
I agree it’s stupid to try and inductively reason that AI models will stop hallucinating, but that was never actually my argument.
This is because “hallucinate” means very different things in the human and LLM context. Humans have false/inaccurate memories all the time, and those are closer to what LLM “hallucination” represents than humam hallucinations are.
We are interacting with multidimensional topological manifolds, and the context we create has a topology within this manifold that constrains the range of output to the fuzzy multidimensional boundary of a geodesic that is the shortest route between our topology and the LLM.
I think some visualisation tools are badly needed, viewing what is happening is for me a very promising avenue to explore with regards to emergent behaviour.
GPT4 says; When interacting with a large language model (LLM) like GPT-4, we engage in a complex and multidimensional process. The context we establish – through our inputs and the responses of the LLM – forms a structured space of possibilities within the broader realm of all possible interactions.
The current context shapes the potential responses of the model, narrowing down the vast range of possible outputs. This boundary of plausible responses could be seen as a high-dimensional 'fuzzy frontier'. The model attempts to navigate this frontier to provide relevant and coherent responses, somewhat akin to finding an optimal path – a geodesic – within the constraints of the existing conversation.
In essence, every interaction with the LLM is a journey through this high-dimensional conversational space. The challenge for the model is to generate responses that maintain coherence and relevancy, effectively bridging the gap between the user's inputs and the vast knowledge that the LLM has been trained on."
There are a few recent Nova specials from PBS that are on YouTube that show just how much bullshit we imagine and make up at any given time. It's mostly our much older and simpler systems below intelligence that keep us grounded in reality.
Memory is far from infallible but human brains do contain knowledge and are capable of introspection. There can be false confidence, sure, but there can also be uncertainty, and that's vital. LLMs just predict the next token. There's not even the concept of knowledge beyond the prompt, just probabilities that happen to fall mostly the right way most of the time.
On the other hand, the problem of getting people to trust AI in sensitive contexts where there could be a lot at stake is non-trivial, and I believe people will definitely demand better-than-human ability in many cases, so pointing out that humans hallucinate is not a great answer. This isn't entirely irrational either: LLMs do things that humans don't, and humans do things that LLMs don't, so it's pretty tricky to actually convince people that it's not just smoke and mirrors, that it can be trusted in tricky situations, etc. which is made harder by the fact that LLMs have trouble with logical reasoning[1] and seem to generally make shit up when there's no or low data rather than answering that it does not know. GPT-4 accomplishes impressive results with unfathomable amounts of training resources on some of the most cutting edge research, weaving together multiple models, and it is still not quite there.
If you want to know my personal opinion, I think it will probably get there. But I think in no way do we live in a world where it is a guaranteed certainty that language-oriented AI models are the answer to a lot of hard problems, or that it will get here really soon just because the research and progress has been crazy for a few years. Who knows where things will end up in the future. Laugh if you will, but there's plenty of time for another AI winter before these models advance to a point where they are considered reliable and safe for many tasks.
I mean this is what I was saying. I just don't think that the technology has to become hallucination-free to be useful. So my bad if I didn't catch the implicit assumption that "any hallucination is a dealbreaker so why even care about security" angle of the post I initially responded to.
My take is simply just that "these things are going to be used more and more as they improve so we better start worrying about supply chain and provenance sooner than later". I strongly doubt hallucination is going to stop them from being used despite the skeptics, and I suspect hallucination is a problem of lack of context moreso than innate shortcomings, but I'm no expert on that front.
And I'm someone who's been asked to try and add AI to a product and had the effort ultimately fail because the model hallucinated at the wrong times... so I well understand the dynamics.
In particular you absolutely can't just continue to extrapolate short-term phenomena out blindly into the future and pretend that has the same level of meaning as things like the sun rising which are the result of fundamental mechanisms that have been observed, explored and understood iteratively better and better over an extremely long time.
There are two things that might change- the sun stops shining, or the earth stops moving. Of the known possible ways for either of those things to happen, we can fairly conclusively say neither will be an issue in our lifetimes.
An asteroid coming out of the darkness of space and blowing a hole in the surface of the earth, kicking up such a dust cloud that we don't see the sun for years is a far more likely, if still statically improbable, scenario.
LLMs, by design, create combinations of characters that are disconnected from the concept of True, False, Right or Wrong.
A more classic example is Freud's deliniation between the id, ego and super-ego. Only the last is built upon imparted cultural mores; the id and ego are purely internal things. Disorders within the ego (excessive defense mechanisms) inhibit perception of what is true and false.
Chatbots / llms don't consider any of these things; they consider only what is the most likely response to a given input?. The result may, by coincidence, happen to be true.
The other thing we have a small number of observations of happening over the last 50 or 60 years but mostly the last 5 years or so. We know some of the mathematical features of the phenomena we are observing but not all and there is a great deal going on that we don't understand (emergence in particular). The things we are seeing contradict most of the academic field of linquistics so we don't have a theoretical basis for them either outside of the maths. The maths (linear algebra) we understand well, but we don't really understand why this particular formulation works so well on language related problems.
Probably the models will improve but we can't naively assume this will just continue. One very strong result we have seen time and time again is that there seems to be an exponential relationship between computation and trainingset size required and capability. So for every delta x increase we want in capability, we seem to pay (at least) x^n (n>1) in computation and training required. That says at some point increases in capability become infeasible unless much better architectures are discovered. It's not clear where that inflection point is.