461–470 of 998 posts
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At least try a different question with similar logic, to ensure this isn't patched into the context since it's going viral.
You can't "patch" LLM's in 4 hours and this is not the kind of question to trigger a web search
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> so you need to tell them the specifics That is the entire point, right? Us having to specify things that we would never specify when talking to a human. You would not start with "The car is functional. The tank is filled with gas. I have my keys." As soon as we are required to do that for the model to any extend that is a problem and not a detail (regardless that those of us, who are familiar with the matter, do bu…
I get that issue constantly. I somehow can't get any LLM to ask me clarifying questions before spitting out a wall of text with incorrect assumptions. I find it particularly frustrating.
https://github.com/Wyattwalls/system_prompts/blob/main/OpenA...
I've used LLMs enough that I have a good sense of their _edges_ of intelligence. I had assumed that reasoning models should easily be able to answer this correctly. And indeed, Sonnet and Opus 4.5 (medium reasoning) say the following: Sonnet: Drive - you need to bring your car to the car wash to get it washed! Opus: You'll need to drive — you have to bring the car to the car wash to get it washed! Gemini 3 Pro (mediu…
> so you need to tell them the specifics That is the entire point, right? Us having to specify things that we would never specify when talking to a human. You would not start with "The car is functional. The tank is filled with gas. I have my keys." As soon as we are required to do that for the model to any extend that is a problem and not a detail (regardless that those of us, who are familiar with the matter, do bu…
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Rule of thumb for who, humans or chatbots? For a human, who has their own wants and values, I think it makes perfect sense to wonder what on earth made the interlocutor ask that.
Rule of thumb for everyone (i.e. both). If I ask you a question, start by assuming I want the answer to the question as stated unless there is a good reason for you to think it's not meant literally. If you have a lot more context (e.g. you know I frequently ask you trick or rhetorical questions or this is a chit-chat scenario) then maybe you can do something differently. I think being curious about the motivations b…
Which sounds like a very common, very understandable reason to think about motivations.
So even in that situation, it isn't simple.
This probably sucks for people who aren't good at theory of mind reasoning. But surprisingly maybe, that isn't the case for chatbots. They can be creepily good at it, provided they have the context - they just aren't instruction tuned to ask short clarifying questions in response to a question, which humans do, and which would solve most of these gotchas.
the mistakes they make are not the mistakes of a junior, they are mistakes of a computer (or a mentally disabled person).
if your job is beeing a redditor, agi is already achieved. it it requires thinking, they are useless.
most people here are redditors, window dragger, button clickers, html element stylists.
All of the latest models I've tried actually pass this test. What I found interesting was all of the success cases were similar to: 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....
Here’s my take: boldness requires the risk of being wrong sometimes. If we decide being wrong is very bad (which I think we generally have agreed is the case for AIs) then we are discouraging strong opinions. We can’t have it both ways.
All of the latest models I've tried actually pass this test. What I found interesting was all of the success cases were similar to: 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....
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.
Out of all conceptual mistakes people make about LLMs, one that needs to die very fast is to assume that you can test what it "knows" by asking a question. This whole thread is people asking different models a question one time and reporting a particular answer, which is the mental model you would use for whether a person knows something or not.
It's not a conceptual mistake when that's what's being advertised. The onus is on AI companies to provide the service they promised, for example, a team of PhDs in my pocket [1]. PhDs know things. 1: https://www.bbc.com/news/articles/cy5prvgw0r1o
Its performance on riddles has always seemed mostly irrelevant to me. Want to know if models can program? Ask them to program, and give them access to a compiler (they can now).
Want to know if it can do PhD level questions? Ask it questions a PhD (or at least grad student) would ask it.
They also reflect the tone and knowledge of the user and question. Ask it about your cat's astrological sign and you get emojis and short sentences in list form. Ask it why large atoms are unstable and you get paragraphs with larger vocabulary. Use jargon and it becomes more of an expert. etc.
The new one is with upside down glass: https://www.tiktok.com/t/ZP89Khv9t/
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You may be anthropomorphizing the model, here. Models don’t have “assumptions”; the problem is contrived and most likely there haven’t been many conversations on the internet about what to do when the car wash is really close to you (because it’s obvious to us). The training data for this problem is sparse.
I'd argue that "assumptions", i.e. the statistical models it uses to predict text, is basically what makes LLMs useful. The problem here is that its assumptions are naive. It only takes the distance into account, as that's what usually determines the correct response to such a question.