I found LLMs to be very useful for literature research. They can find relevant prior work that you can’t find with a search engine because you don’t know the right keywords. This can be a significant force multiplier.
They also seem potentially useful for quickly producing code for numerical tests of conjectures, but I only started experimenting with that.
Other use cases where I found LLMs beneficial:
Taking a photo of a menu in French (or providing a link to it) and asking it which dishes are vegan.
Recommending movies (I am a little wary of some kind of meme poisoning, but I don’t watch movies very often, so seems ok).
That said, I do agree that early adopters seem like they’re overeager and maybe even harming themselves in some way.
I’ve been trying to use Deep Research tools as a way to find hyper-specific fiction recommendations as well. The results have been mixed. They don’t seem to be very good at grokking the hyper-specificness of what you’re looking for, usually they have a heavy bias towards the popular stuff that outweighs what you actually requested[1], and if you ask them to look for obscure works, they tend to output garbage instead of hidden gems (because no taste).
It did produce good results a few times, though, and is only slightly worse than asking for recommendations on r/rational. Possibly if I iterate on the prompt a few times (e. g., explicitly point out the above issues?), it’ll actually become good.
Like, suppose I’m looking for some narrative property X. I want to find fiction with a lot of X. But what the LLM does is multiplying the amount of X in a work by the work’s popularity, so that works that are low in X but very popular end up in its selection.
I tend to have some luck with concrete analogies sometimes. For example I asked for the equivalent of Tonedeff (His polymer album is my favorite album) in other genres and it recommended me Venetian Snares. I then listened to some of his songs and it seemed like the kind of experimental stuff where I might find something I find interesting. Venetian Snares has 80k monthly listeners while Tonedeff has 14K, so there might be some weighting towards popularity, but that seems mild.
I can think of reasons why some would be wary, and am waried of something which could be called “meme poisoning” myself when I watch moves, but am curious what kind of meme poisoning you have in mind here.
I found LLMs to be very useful for literature research. They can find relevant prior work that you can’t find with a search engine because you don’t know the right keywords. This can be a significant force multiplier.
They also seem potentially useful for quickly producing code for numerical tests of conjectures, but I only started experimenting with that.
Other use cases where I found LLMs beneficial:
Taking a photo of a menu in French (or providing a link to it) and asking it which dishes are vegan.
Recommending movies (I am a little wary of some kind of meme poisoning, but I don’t watch movies very often, so seems ok).
That said, I do agree that early adopters seem like they’re overeager and maybe even harming themselves in some way.
I’ve been trying to use Deep Research tools as a way to find hyper-specific fiction recommendations as well. The results have been mixed. They don’t seem to be very good at grokking the hyper-specificness of what you’re looking for, usually they have a heavy bias towards the popular stuff that outweighs what you actually requested[1], and if you ask them to look for obscure works, they tend to output garbage instead of hidden gems (because no taste).
It did produce good results a few times, though, and is only slightly worse than asking for recommendations on r/rational. Possibly if I iterate on the prompt a few times (e. g., explicitly point out the above issues?), it’ll actually become good.
Like, suppose I’m looking for some narrative property X. I want to find fiction with a lot of X. But what the LLM does is multiplying the amount of X in a work by the work’s popularity, so that works that are low in X but very popular end up in its selection.
I tend to have some luck with concrete analogies sometimes. For example I asked for the equivalent of Tonedeff (His polymer album is my favorite album) in other genres and it recommended me Venetian Snares. I then listened to some of his songs and it seemed like the kind of experimental stuff where I might find something I find interesting. Venetian Snares has 80k monthly listeners while Tonedeff has 14K, so there might be some weighting towards popularity, but that seems mild.
I can think of reasons why some would be wary, and am waried of something which could be called “meme poisoning” myself when I watch moves, but am curious what kind of meme poisoning you have in mind here.