e.g. "Drive. Most car washes require the car to be present to wash,..."
Only most?!
They have an inability to have a strong "opinion" probably because their post training, and maybe the internet in general, prefer hedged answers....
>You should drive your car to the car wash. Even though it's only 50 meters away (which is very close), you'll need your car physically present at the car wash to get it washed. If you walk there, you'll arrive without your car, which wouldn't accomplish your goal of getting it washed.
>You'll need to drive your car to the car wash. While 50 meters is a very short distance (just a minute's walk), you need your car to actually be at the car wash to get it washed. Walking there without your car wouldn't accomplish your goal!
etc. The reasoning never second-guesses it either.
A shame they're turning it of in 2 days.
What opinion? It's evaluation function simply returned the word "Most" as being the most likely first word in similar sentences it was trained on. It's a perfect example showing how dangerous this tech could be in a scenario where the prompter is less competent in the domain they are looking an answer for. Let's not do the work of filling in the gaps for the snake oil salesmen of the "AI" industry by trying to explain its inherent weaknesses.
It just turns out that there's quite a bit of knowledge and understanding baked into the relationships of words to one another.
LLMs are heavily influenced by preceding words. It's very hard for them to backtrack on an earlier branch. This is why all the reasoning models use "stop phrases" like "wait" "however" "hold on..." It's literally just text injected in order to make the auto complete more likely to revise previous bad branches.
But they are literally predicting the next token. They do nothing else.
Also if you think they were just predicting the next token in 2021, there has been no fundamental architecture change since then. All gains have been via scale and efficiency optimisations (not to discount that, an awful lot of complexity in both of these)
The models that had access to search got ot right.But, then were just dealing with an indirect version of Google.
(And they got it right for the wrong reasons... I.e this is a known question designed to confuse LLMs)
There’s a level of earnestness here that tickles my brain.
There is such a thing as "mobile car wash" where they come to you, so "most" does seem appropriate.
And it is the kind of things a (cautious) human would say.
For example, that could be my reasoning: It sounds like a stupid question, but the guy looked serious, so maybe there are some types of car washes that don't require you to bring your car. Maybe you hand out the keys and they pick your car, wash it, and put it back to its parking spot while you are doing your groceries or something. I am going to say "most" just to be sure.
Of course, if I expected trick questions, I would have reacted accordingly, but LLMs are most likely trained to take everything at face value, as it is more useful this way. Usually, when people ask questions to LLMs they want an factual answer, not the LLM to be witty. Furthermore, LLMs are known to hallucinate very convincingly, and hedged answers may be a way to counteract this.
I mean I can imagine a scenario where they have pipe of 50m which is readily available commercially?
What if AI developed sarcasm without us knowing… xD
I guess it gives the correct answer now. I also guess that these silly mistakes are patched and these patches compensate for the lack of a comprehensive world model.
These "trap" questions dont prove that the model is silly. They only prove that the user is a smartass. I asked the question about pregnancy only to to show a friend that his opinion that LLMs have phd level intelligence is naive and anthropomorphic. LLMs are great tools regardless of their ability to understand the physical reality. I don't expect my wrenches to solve puzzles or show emotions.
