“Unproven”, I don’t get how anyone can use LLMs and come away with this opinion. There is simply no better way to do a fuzzy search than by typing a vague prompt into an LLM.

I was trying to find a movie title the other day, only remembered it had Lime in the title and had a Jack-the-Ripper setting. ChatGPT found it easily. Sure you have to fact check the results, but there’s undeniable value there.

I was trying to remember the name of a book series I read as a child in the late 80s/early 90s. I gave ChatGPT part of a title (it had Scorpion and a few other words in the title), a few plot points, and the decade it was published, and asked for an ISBN. It confidently returned a book with Scorpion in the title, a short plot summary, published in 1983, an ISBN, even an author, and it was all entirely made up. It took me a few minutes to realize this when my searches on Amazon and library websites turned up nothing.

FWIW, you might also try searching https://www.worldcat.org/ , which is a large collection of library catalogs.

Which model did you use? I never get hallucinations like that

If you asked a job candidate the same question, does their wrong response make them a bad candidate for the job ?

If I asked a job candidate any question and they confidently replied with a set of facts that were entirely made up, I would not consider them for any position under any circumstances.

Exactly -- people are lazy and want to skip the fact checking. They want to be sold a divine oracle.

Further to your point, I like to try ChatGPT on:

What is the word for a {language, rhetorical device, figure of speech} in which ... <various properties>?"

Then forward-look up the results on more authoritative, accountable sites.

I’m not sure this is the best example of the power of an LLM. Not denying there are actual uses cases, but simply searching Google “Lime movie Jack the Ripper” will display the answer in the first result (and I imagine would have been able to do that for the past 10+ years)

I put "movie title the other day, only remembered it had Lime in the title and had a Jack-the-Ripper" in Google and the first answer is "The Limehouse Golem".

Is that it ? Dunno if behind the scenes it was using an LLM or "classical" search.

Value is there, but it does not match the hype as of today

Nothing ever matches the hype. That's what hype is.

Literally short for hyperbole which means "exaggerated statements or claims not meant to be taken literally."

>a situation in which something is advertised and discussed in newspapers, on television, etc. a lot in order to attract everyone's interest:

>Nothing ever matches the hype. That's what hype is.

Imo thats crazy take, there are definitely things that live up to the hype

there's always someone for whom it doesn't

Who cares about single person?

Just look at the majority and how big that group is

The issue that LLMs are constantly hallucinating and are not capable following long term rules. Since its still niche, its not a problem but what if professionals like Lawyers or doctors start using it day to day, then we are in trouble. I wouldn't go as far as saying its useless but its effectiveness is very close to zero in most fields not related to spamming.

It depends on your line of work. If it involves determining factual accuracy LLMs may not help: https://nondeterministic.computer/@martin/112326227079145176

LLMs are good at synthesis and generation.

If you try to replace your whole job with an LLM, yes you will have problem. I work in IT, I use ChatGPT daily to spit out scripts, ask it to come up with function names, convert code from a technology to another, ask it to do minor refactoring that I don't know how to do.

I can immediately validate the output, learn from it, and even work with techs I'm not familiar with.

So you use it for generation where validation is easy. If validation were harder, the calculus would change.

I also get a lot of mileage out of it, but it is important to recognize its shortcomings.

of course, I don't try to replace my whole job out of it. ChatGPT is 'good' at doing statistical analysis with python on a given dataset, that can help in the harder task you quoted "distinguishing between ideas that are correct, and ideas that are plausible-sounding but wrong". The job itself cannot be easily verified, but you can use LLM on a subset of theses tasks.

There are plenty of use cases where validation is easy.