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...
You see people (business people who are enthusiastic about tech, often), claiming that these bots are the new Google and Wikipedia, and that you’re behind the times if you do, what amounts, to looking up information yourself.
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.
They are only the new Google when they are told to process and summarize web searches. When using trained knowledge they're about as reliable as a smart but stubborn uncle.
Pretty much only search-specific modes (perplexity, deep research toggles) do that right now...
Out of curiosity, is this a question you think Google is well-suited to answer^? How many Wikipedia pages will you need to open to determine the answer?
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.
Unlike the friendly bot, if I can’t find credible enough sources I’ll stay with an honest “I don’t know”, instead of praising the genius of whoever asked and then making something up.
Sure, but this is a false dichotomy. If I get an unsourced answer from ChatGPT, my response will be "eh you can't trust this, but ChatGPT thinks x"
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.
It's not how I use LLMs. I have a family member who often feels the need to ask ChatGPT almost any question that comes up in a group conversation (even ones like this that could easily be searched without needing an LLM) though, and I imagine he's not the only one who does this. When you give someone a hammer, sometimes they'll try to have a conversation with it.
I'll respond to this bait in the hopes that it clicks for someone how to _not_ use an LLM..
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.
> 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").
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.
Yeah, the "you're using it wrong" argument falls flat on its face when the technology is presented as an all-in-one magic answer box. Why give these companies the benefit of the doubt instead of holding them accountable for what they claim this tech to be? https://www.youtube.com/watch?v=9bBfYX8X5aU
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.
Whenever I read something like this I do definitely think "you're using it wrong". This question would've certainly tripped up earlier models but new ones have absolutely no issue making this with sources for each question. Example:
(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.
Fwiw, if you can use a thinking model, you can get them to do useful things. Find specific webpages (menus, online government forms - visa applications or addresses, etc).
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)
You either give them the option to search the web for facts or you ask them things where the utility/validity of the answer is defined by you (e.g. 'summarize the following text...') instead of the external world.
