alt Hacker NewsAll the people responding saying "You would never ask a human a question like this" - this question is obviously an extreme example. People regularly ask questions that are structured poorly or have a lot of ambiguity. The point of the poster is that we should expect that all LLM's parse the question correctly and respond with "You need to drive your car to the car wash."
People are putting trust in LLM's to provide answers to questions that they haven't properly formed and acting on solutions that the LLM's haven't properly understood.
And please don't tell me that people need to provide better prompts. That's just Steve Jobs saying "You're holding it wrong" during AntennaGate.
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>People regularly ask questions that are structured poorly or have a lot of ambiguity.
The difference between someone who is really good with LLM's and someone who isn't is the same as someone who's really good with technical writing or working with other people.
Communication. Clear, concise communication.
And my parents said I would never use my English degree.
> All the people responding saying "You would never ask a human a question like this"
That's also something people seem to miss in the Turing Test thought experiment. I mean sure just deceiving someone is a thing, but the simplest chat bot can achieve that. The real interesting implications start to happen when there's genuinely no way to tell a chatbot apart.
That’s not the problem with this post.
The problem is that most LLM models answer it correctly (see the many other comments in this thread reporting this). OP cherry picked the few that answered it incorrectly, not mentioning any that got it right, implying that 100% of them got it wrong.
I recently asked an AI a chemistry question which may have an extremely obvious answer. I never studied chemistry so I can't tell you if it was. I included as much information about the situation I found myself in as I could in the prompt. I wouldn't be surprised if the ai's response was based on the detail that's normally important but didn't apply to the situation, just like the 50 meters
This is the LLM equivalent of a riddle, eg: “A farmer has 17 sheep. All but 9 die. How many are left?”
Exactly! The problem isn't this toy example. It's all of the more complicated cases where this same type of disconnect is happening, but the users don't have all of the context and understanding to see it.
> All the people responding saying "You would never ask a human a question like this"
It would be interesting to actually ask a group a people this question. I'm pretty sure a lot of people would fail.
It feels like one of those puzzles which people often fail. E.g: 'Ten crows are sitting on a power line. You shoot one. How many crows are left to shoot?' People often think it's a subtraction problem and don't consider that animals flee after gunshots. (BTW, ChatGPT also answers 9.)
Other leading LLMs do answer the prompt correctly. This is just a meaningless exercise in kicking sand in OpenAI's face. (Well-deserved sand, admittedly.)
This reminds me of the old brain-teaser/joke that goes something like 'An airplane crashes on the boarder of x/y, where do they bury the survivors?' The point being that this exact style of question has real examples where actual people fail to correctly answer it. We mostly learn as kids through things like brain teasers to avoid these linguistic traps, but that doesn't mean we don't still fall for them every once in a while too.