Comment by GuB-42 | Hacker News Reader

Comment by GuB-42 | Hacker News Reader

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Humans can be deceptive, but it is usually deliberate. We can also honestly make things up and present them as fact but it is not that common, we usually say that we don't know. And generally, lying is harder for us than telling the truth, in the sense that making a consistent but false narrative requires effort.

For LLMs, making stuff up is the default, one can argue that it is all they do, it just happens to be the truth most of the times.

And AFAIK, what I would call the "real" Turing test hasn't been shattered by far. The idea is that the interrogator and the human subject are both experts and collaborate against the computer. They can't cheat by exchanging secrets, but anything else is fair game.

I think it is important because the Turing test has already been "won" by primitive algorithms acting clueless to interrogators who were not aware of the trick. For me, this is not really a measure of computer intelligence, more like a measure of how clever the chatbot designers were at tricking unsuspecting people.

> we usually say that we don't know

I think this is one of the distinguishing attributes of human failures. Human failures have some degree of predictability. We know when we aren't good at something, we then devise processes to close that gap. Which can be consultations, training, process reviews, use of tools etc.

The failures we see in LLMs are distinctly of a different nature. They often appear far more nonsensical and have more of a degree of randomness.

The LLMs as a tool would be far more useful if they could indicate what they are good at, but since they cannot self reflect over their knowledge, it is not possible. So they are equally confident in everything regardless of its correctness.

I think the last few years are a good example of how this isn't really true. Covid came around and everyone became an epidemiologist and public health expert. The people in charge of the US government right now are also a perfect example. RFK Jr. is going to get to the bottom of autism. Trump is ruining the world economy seemingly by himself. Hegseth is in charge of the most powerful military in the world. Humans pretending they know what they're doing is a giant problem.

They are different contexts of errors. Take any of these humans in your example, and give them an objective task, such as take any piece of literal text and reliably interpret its meaning and they can do so.

LLMs cannot do this. There are many types of human failures, but we somewhat know the parameters and context of those failures. Political/emotional/fear domains etc have their own issues, but we are aware of them.

However, LLMs cannot perform purely objective tasks like simple math reliably.

> Take any of these humans in your example, and give them an objective task, such as take any piece of literal text and reliably interpret its meaning and they can do so.

I’m not confident that this is so. Adult literacy surveys (see e.g. https://nces.ed.gov/use-work/resource-library/report/statist...) consistently show that most people can’t reliably interpret the meaning of complex or unfamiliar text. It wouldn’t surprise me at all if RFK Jr. is antivax because he misunderstands all the information he sees about the benefits of vaccines.

> most people can’t reliably interpret the meaning of complex or unfamiliar text

But LLMs fail the most basic tests of understanding that don't require complexity. They have read everything that exists. What would even be considered unfamiliar in that context?

> RFK Jr. is antivax because he misunderstands all the information he sees about the benefits of vaccines.

These are areas where information can be contradictory. Even this statement is questionable in its most literal interpretation. Has he made such a statement? Is that a correct interpretation of his position?

The errors we are criticizing in LLMs are not areas of conflicting information or difficult to discern truths. We are told LLMs are operating at PhD level. Yet, when asked to perform simpler everyday tasks, they often fail in ways no human normally would.

> But LLMs fail the most basic tests of understanding that don't require complexity.

Which basic tests of understanding do state-of-the-art LLMs fail? Perhaps there's something I don't know here, but in my experience they seem to have basic understanding, and I routinely see people claim LLMs can't do things they can in fact do.

Take a look at this vision test - https://www.mindprison.cc/i/143785200/the-impossible-llm-vis...

It is an example that shows the difference between understanding and patterns. No model actually understands the most fundamental concept of length.

LLMs can seem to do almost anything for which there are sufficient patterns to train on. However, there aren't infinite patterns available to train on. So, edge cases are everywhere. Such as this one.

I don't see how this shows that models don't understand the concept of length. As you say, it's a vision test, and the author describes how he had to adversarially construct it to "move slightly outside the training patterns" before LLMs failed. Doesn't it just show that LLMs are more susceptible to optical illusions than humans? (Not terribly surprising that a language model would have subpar vision.)

But it is not an illusion, and the answers make no sense. In some cases the models pick exactly the opposite answer. No human would do this.

Yes, outside the training patterns is the point. I have no doubt if you trained LLMs on this type of pattern with millions of examples it could get the answers reliably.

The whole point is that humans do not need data training. They understand such concepts from one example.

Yeah humans can be terrible. I am not sure what is the argument here. Does that make it ok to use software that can be just as terrible?

Depends on the context. I've seen a lot of value from deploying LLMs in things like first-line customer support, where a suggestion that works 60% of the time is plenty valuable, especially if the bot can crank it out in 10 seconds when a human would take 5-10 minutes to get on the phone.

I too have seen economic value be collected by terrible things that absolutely not exist in my opinion. Your example fits the bill.

Profitability is not the absolute measure of what should exist.

I'm not sure what you're referring to, since profitability wasn't a metric I used. I agree not all profitable things should exist, but increasing the availability of customer support seems to me like a clearly good thing.

Perhaps you're thinking that profit-chasing is the only reason companies don't offer good customer support today? That's not accurate. Providing enough smart, well-resourced human beings to answer every question your customers can come up with is a huge operational challenge, unless your product is absolutely dead simple or you're small enough to make random employees help in their spare time.

> I've seen a lot of value from deploying LLMs in things like first-line customer support, where a suggestion that works 60% of the time is plenty valuable

Valuable to whom? How so?

At that rate I would end my business with such a company.

If you claim such a terrible level of costumer support is valuable, I question your judgement of value.

Valuable to customers, because it allows them to get instant advice that will often solve their problem. I strongly suspect that some companies you do business with have already integrated LLMs into their customer support workflow - it's very common these days.

Hard disagree on instant suggestions with 40% miss rate being valuable. I want support not half backed guesses.

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