One recent catastrophic failure I found: Ask an LLM to generate 10 pieces of data. Then in a second input, ask it to select (say) only numbers 1, 3, and 5 from the list. The LLM will probably return results numbered 1, 3, and 5, but chances are at least one of them will actually copy the data from a different number.
LLMs are looking at typical constructions of text, not an understanding of what it means. If you ask it what color the sky is, it'll find what text usually follows a sentence like that, and tries to construct a response from it.
If you ask it the answer to a math question, the only way it could reliably figure it out is if it has in its database an exact copy of that math question. Asking it to choose things from a list is kinda like that, but one could imagine that the designers would try to supplement that manually with a different technique from pure LLM.
Unless of course we rephrase it as "when I roll 2d6, why do I sometimes get snake eyes?"
https://hachyderm.io/@inthehands/112006855076082650
> You might be surprised to learn that I actually think LLMs have the potential to be not only fun but genuinely useful. “Show me some bullshit that would be typical in this context” can be a genuinely helpful question to have answered, in code and in natural language — for brainstorming, for seeing common conventions in an unfamiliar context, for having something crappy to react to.
> Alas, that does not remotely resemble how people are pitching this technology.