So it seems like this particular implementation is not particularly good but the general chat-as-search might fare better, though there are definitely many ways to get ChatGPT to say nonsense.
So it seems like this particular implementation is not particularly good but the general chat-as-search might fare better, though there are definitely many ways to get ChatGPT to say nonsense.
What, you think they're checking the veracity of sources they find on Google? Get a grip...
I am also wondering:
1) how resilient chat-to-search will be SEO spam in the future, 2) whether people will be less likely to publish content if chat is supposed to spit out its summaries and, potentially, reduce traffic to their sites.
https://www.tomshardware.com/news/artifical-intelligence-adv...
> whether people will be less likely to publish content if chat is supposed to spit out its summaries and, potentially, reduce traffic to their sites.
Good. Today, kind humans summarize long winded articles in the comment section and often save me a click. Sometimes I'm that human.
It does seem like the biggest failure cases of chat are happening when we have not yet incorporated one of our apps (like weather or directions).
Richard from YOU here.
https://en.wikipedia.org/wiki/Old_Swiss_Confederacy#:~:text=....
SAT: https://twitter.com/davidtsong/status/1598767389390573569
LSAT 40th percentile: https://twitter.com/pythonprimes/status/1599875927625764864
Can't find the math one, but I think it was posted here a few days ago.
I didn't end up following the recipe because (just like the compiling problems with its code suggestions) I wasn't willing to spend a few hours and have it turn out bad. I would love to see a YouTube channel of someone following AI recipes, I wonder if it could come up with some unique foods.
One thing I do enjoy using it for is providing healthy meal ideas.
Both Chat and You illustrate garbage in garbage out, you throw something nonsensical or without context and it doesn't know what you mean but also doesn't know that it doesn't know. Both assume you ask for a distance from a city to itself.
>How many miles away is Boston from Boston?
>Boston is located in Massachusetts, United States, and it is approximately 0 miles away from itself. It is not possible for a city or location to be a certain distance away from itself, as it is the point of reference for measuring distance. If you are asking about the distance between two different locations both named Boston, it would be necessary to specify which two Bostons you are referring to.
There are tons of places named Boston, yet the model assumes you want to know about the distance from US city to itself. This seems pretty stupid but I guess somehow you find the question and the answer meaningful. Care to explain your reasoning?
(Unless you are a language model too, in that case it all makes sense)
Literally the only instance where a question about distance makes sense is in a context of two different places.
The model needs to reply with something and here it tells the user the assumptions it made. The only alternative I can think of would be to arbitrarily choose two Bostons but my guess is it is more likely that people making this request would intend to include the same location in both.
One use for ChatGPT is to give a prompt with lists of data. It is convenient if ChatGPT handles the trivial edge cases correctly so that the user doesn't need to do preprocessing.
"Can you give the distance in miles between a list of pairs of cities? Want the output formatted like this:
city a, city b, 10 city b, city c, 100 ...
The pairs of cities are:
New York City, Los Angeles
Chicago, Los Angeles
Chicago, Paris
Chicago, Boston
Boston, Boston
Boston, Los Angeles"
Response from ChatGPT:
"Sure! Here is the list of pairs of cities and their distances in miles:
New York City, Los Angeles, 2,798
Chicago, Los Angeles, 2,054
Chicago, Paris, 4,973
Chicago, Boston, 1,099
Boston, Boston, 0
Boston, Los Angeles, 2,905
Please note that these are approximate distances and may vary slightly due to factors such as route taken and Earth's curvature."
me again: if it asked you to clarify exactly which city you are referring to for each without making assumptions, making requests would be pretty tedious. Sometimes, you are processing data that starts out ambiguous to you anyway.
> The model needs to reply with something and here it tells the user the assumptions it made.
No, it didn't "tell" its assumption (where did it output "I assume you mean you want to know the distance from US city to itself"?). It lacks the awareness to even know the assumption is made let alone that it's nonsensical. But it's clear from its responses what the assumption was: that you want to know the distance between a city and itself.
In your second prompt you provided the context (a list of exclusively US cities). With context established, as I said in the first place, it's no longer pure garbage in.
Honestly you don't need to copy-paste more of that stuff, it does nothing to support your counter-argument against mine that if you give it garbage, it will respond with garbage.
Just reread what it told me; it told me it is zero miles away from itself but it was a nonsensical query and that one should be more specific if the cities being compared are different.
> But it's clear from its responses what the assumption was: that you want to know the distance between a city and itself.
If you ask for the distance between just Boston and London, it assumes Boston, MA and London, UK just like Google does. Neither asks if I want the distance between London, UK and Boston, England. Same thing for Portland and Boston, even though Portland, Maine is much closer to Boston, MA. I think both ChatGPT and Google just assume the largest cities.
Not sure if you got my point about the list. In that situation I'd find the zero miles to be useful because otherwise I'd need to remember to exclude some entries from the list and add back those records doing the trivial transformation afterward.
Another way to think of it is if I wrote a function for you that asks if the number n is divisible by 17, wouldn't you want it to handle the trivial case of 'Is 17 divisible by 17?' over a CSV rather than excluding the trivial examples and handling them manually? Trivial case handling is important for LLMs. Is also useful if you're trying to test its logical consistency.
>The reason I think you're a LLM is that you somehow believe that knowing a distance between a place and itself is meaningful. It's still the case.
Based on my posting history (13 years) and username (real name), chances are pretty good I'm human. I mentioned my reasons; sorry I couldn't communicate it clearly enough. Very likely I will disengage from this thread now. Also, don't appreciate being called a bot; think people doing that makes Hacker News worse.
It made the least sane assumption. If it was aware of that, it would have used a more reasonable one (eg. two cities).