[BalinKing]

> Claude is like having my own college professor.

I don't use Claude, so maybe there's a huge gap in reliability between it and ChatGPT 4o. But with that disclaimer out of the way, I'm always fairly confused when people report experiences like these—IME, LLMs fall over miserably at even very simple pure math questions. Grammatical breakdowns of sentences (for a major language like Japanese) are also very hit-or-miss. I could see an LLM taking the place of, like, an undergrad TA, but even then only for very well-trod material in its training data.

(Or maybe I've just had better experiences with professors, making my standard for this comparison abnormally high :-P )

EDIT: Also, I figure this sort of thing must be highly dependent on which field you're trying to learn. But that decreases the utility of LLMs a lot for me, because it means I have to have enough existing experience in whatever I'm trying to learn about so that I can first probe whether I'm in safe territory or not.

[joseda-hg]

Major in the context of Japanese is rough, I can see a significant drop in quality when interacting with the same model in say Spanish vs English

For as rich a culture the Japanese have, there's only about 1XX million speakers and the size of the text corpus really matters here, the couple billion of English speakers are also highly motivated to choose English over anything else because Lingua Franca has homefield advantage

To use LLM's efectively you have to work with knowledge of their weaknesses, Math is a good example, you'll get better results from Wolphram Alpha even for the simple things, which is expected

Broad reasoning and explanations tend to be better than overly specific topics, the more common a language, the better the response If a topic has a billion tutorials online, an LLM has a really high chance of figuring out first try

Be smart with the context you provide, the more you actively constrain an LLM, the more likely it is to work with you I have friends that just use it to feed class notes to generate questions and probe it for blindspots until they're satisfied, the improvements on their grade s make it seem like a good approach, but they know that just feeding responses to the LLM isn't trustworthy, so they do and then they also check by themselves, the extra time valuable by itself, if just to improve familiarity with the subject

[satvikpendem]

> LLMs fall over miserably at even very simple pure math questions

They are language models, not calculators or logic languages like Prolog or proof languages like Coq. If you go in with that understanding, it makes a lot more sense as to their capabilities. I would understand the parent poster to mean that they are able to ask and rapidly synthesize information from what the LLM tells them, as a first start rather than necessarily being 100% correct on everything.

[BalinKing]

Of course that's fair advice in itself, but the parent specifically equated them to a "college professor."

[justinclift]

Maybe that should be "college art professor" then? :)

[maybesomaybenot]

I think a lot of these people object to AI probably see the gross amounts of energy it is using, or the trillions of dollars going to fewer than half a dozen men (most american, mostly white).

But, once you've had AI help you solve some gnarly problems, it is hard not to be amazed.

And this is coming from a gal who thinks the idea of self-driving cars is the biggest waste of resources ever.

[BalinKing]

(EDIT: Upon rereading this, it feels unintentionally blunt. I'm not trying to argue, and I apologize if my tone is somewhat unfriendly—that's purely a reflection of the fact that I'm a bad writer!)

Sorry, maybe I should've been clearer in my response—I specifically disagree with the "college professor" comparison. That is to say, in the areas I've tried using them for, LLM's can't even help me solve simple problems, let alone gnarly ones. Which is why hearing about experiences like yours leaves me genuinely confused.

I do get your point about people disagreeing with modern AI for "political" reasons, but I think it's inaccurate to lump everyone into that bucket. I, for one, am not trying to make any broader political statements or anything—I just genuinely can't see how LLMs are as practically useful as other people claim, outside of specific use cases.

[maybesomaybenot]

Like I made very clear, it is great at some things and terrible at others.

YMMV. /shrugs/