The main use case for LLMs is writing text nobody wanted to read. The other use case is summarizing text nobody wanted to read. Except they don’t do that either. The Australian Securities and…
The problem is not the LLMs, but what people are trying to do with them.
They are currently spoons, but people are desperately wishing they were katanas.
They work really well for soup, but they can't cut steak. But they're being hyped as super ninja steak knives, and people are getting pissed when they can't cut steak.
If you give them watery, soupy tasks they can do successfully, they can lighten your workload, as long as you're aware of what they are and aren't good at.
What people want LLMs to be able to do, ie. "Steak" tasks:
write complex documents
apply complex knowledge/rules to a situation
Write complex code and create entire programs based on vague description
What LLMs can currently do ie. "Soup" tasks:
check this document and fix all spelling, punctuation and grammatical errors
summarise this paragraph as dot points
write a python program that sorts my photographs into folders based on the year they were taken
Half of Lemmy is hyping katanas, the other half is yelling "Why won't my spoon cut this steak?!! AI is so dumb!!!"
Update: wow, the pure vitriol pouring out of the replies is just stunning. Seems there are a lot of you out there who have, in one way or another, tied your ego very strongly to either the success or failure of AI.
Take a step back, friends, and go outside for a while.
Clearly this post is about LLMs not succeeding at this task, but anecdotally I've seen it work OK and also fail. Just like humans, which is the benchmark but they are faster.