Why is this top comment.. this isn't a question you ask an LLM. But I know, that's how people are using them and is the narrative which is sold to us...
We’re preaching to the choir by being insistent here that you prompt these things to get a “vibe” about a topic rather than accurate information, but it bears repeating.
Pretty much only search-specific modes (perplexity, deep research toggles) do that right now...
When folks are frustrated because they see a bizarre question that is an extreme outlier being touted as "model still can't do _" part of it is because you've set the goalposts so far beyond what traditional Google search or Wikipedia are useful for.
^ I spent about five minutes looking for the answer via Google, and the only way I got the answer was their ai summary. Thus, I would still need to confirm the fact.
And then you can use that to quickly look - does that player have championships mentioned on their wiki?
It's important to flag that there are some categories that are easy (facts that haven't changed for ten years on Wikipedia) for llms, but inference only llms (no tools) are extremely limited and you should always treat them as a person saying "I seem to recall x"
Is the ux/marketing deeply flawed? Yes of course, I also wish an inference-only response appropriately stated its uncertainty (like a human would - eg without googling my guess is x). But among technical folks it feels disingenuous to say "models still can't answer this obscure question" as a reason why they're stupid or useless.
Asking "them"... your perspective is already warped. It's not your fault, all the text we've previously ever seen is associated with a human being.
Language models are mathematical, statistical beasts. The beast generally doesn't do well with open ended questions (known as "zero-shot"). It shines when you give it something to work off of ("one-shot").
Some may complain of the preciseness of my use of zero and one shot here, but I use it merely to contrast between open ended questions versus providing some context and work to be done.
Some examples...
- summarize the following
- given this code, break down each part
- give alternatives of this code and trade-offs
- given this error, how to fix or begin troubleshooting
I mainly use them for technical things I can then verify myself.
While extremely useful, I consider them extremely dangerous. They provide a false sense of "knowing things"/"learning"/"productivity". It's too easy to begin to rely on them as a crutch.
When learning new programming languages, I go back to writing by hand and compiling in my head. I need that mechanical muscle memory, same as trying to learn calculus or physics, chemistry, etc.
That is the usage that is advertised to the general public, so I think it's fair to critique it by way of this usage.
I like to ask these chatbots to generate 25 trivia questions and answers from "golden age" Simpsons. It fabricates complete BS for a noticeable number of them. If I can't rely on it for something as low-stakes as TV trivia, it seems absurd to rely on it for anything else.
https://chatgpt.com/share/69160c9e-b2ac-8001-ad39-966975971a...
(the 7 minutes thinking is because ChatGPT is unusually slow right now for any question)
These days I'd trust it to accurately give 100 questions only about Homer. LLMs really are quite a lot better than they used to be by a large margin if you use them right.
The best thing about the latter is search ads have extremely unfriendly ads that might charge you 2x the actual fee, so using Google is a good way to get scammed.
If I'm walking somewhere (common in NYC) I often don't mind issuing a query (what's the salt and straw menu in location today) and then checking back in a minute. (Or.... Who is playing at x concert right now if I overhear music. It will sometimes require extra encouragement - "keep trying" to get the right one)
I play once or twice a week and it's definitely worth $20/mo to me