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Ask HN: What is one simple thing LLMs are insanely bad at?

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Re: Ask HN: What is one simple thing LLMs are insanely bad at?

#24

Having a spatial understanding from an ASCII map, while doing long term planning. Just try making an AI play nethack or similar

Convert it to an image on the fly to feed it into a vision language model and I expect it would work just fine.

Re: Ask HN: What is one simple thing LLMs are insanely bad at?

#26
I've had a lot of trouble when it comes to sorting out UIs. I've tried with an iOS game and also a TypeScript app with UI elements from libraries like ReactFlow. The usual models can sometimes fix or change things based on screenshots but more often than not they just don't "get it" (e.g. certain shapes on a plane are overlapping, which I don't want, the models can't fix what they can't "see").

I've had some luck on the web app side if I use playwright or similar for the model to interact with but still far from efficient.

Re: Ask HN: What is one simple thing LLMs are insanely bad at?

#27

They don't generate keyword search queries very well. They can overcome this by brute force but if you watch what they search you will cringe. nhl toronto scores nhl hockey toronto scores "nhl hockey" toronto score today nhl "hockey score toronto" "hockey" who won toronto etc. Somehow being good at semantic search makes them bad at keyword search, for whatever reason.

Reminds me of using AltaVista search back in the day. Yes, it was that bad.

Re: Ask HN: What is one simple thing LLMs are insanely bad at?

#28

Video game tips. Constant mistakes and hallucinations, in my experience. Seen this across a lot of different games. Even in really well documented games, such as OSRS (which has multiple fantastic wikis). Anno 1800 was a recent one I had trouble with, using Claude Opus. Completely made up game mechanics. Rainbow Six Siege, too.

I used ChatGPT on nfs heat and was fine

Re: Ask HN: What is one simple thing LLMs are insanely bad at?

#29
I have been working on a personal benchmark suite to test new models and ironically one thing all the models are bad at is writing new benchmark tasks. I guess it’s the different layers of abstraction between the task and how it’s evaluated? Or maybe just a lack of “imagination”

Tasks it writes are typically too easy but also it utterly fails to see how a different model might misunderstand a vague part of the prompt.

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