The thing about being "pedantic" with LLMs is this is actually a very good thing to do! These systems output very plausible sounding text and we can trick ourse

The thing about being "pedantic" with LLMs is this is actually a very good thing to do! These systems output very plausible sounding text and we can trick ourselves in to believing these things behave a like a human, but they fail in subtle ways that we need to be very careful of. Finding examples where it is subtly wrong is very important to understanding what we should expect of these models. If we ignore subtle errors, we could make the mistake of deploying these in situations where those subtle errors can get people hurt. I was just listening to the TWML podcast last night with a Microsoft researcher who studies LLMs, and this "pedantic" analysis is exactly the kind of thing professional LLM engineers concern themselves with! They do all kinds of manipulation and then run standardized tests to see exactly how they perform, because they would like to use these for general purpose computing tasks and subtle errors are exactly the kind of thing they want to eliminate! It's funny to see people complaining about this when this kind of analysis is very important to improving these systems. I want to remind people not to romanticize these systems or get attached to our expectations of what we think it should be doing. They are a novel computer system and we should be happy to objectively analyze their performance.