zlacker

>>ssgodd+(OP)
I have deeply mixed feelings about the way LLMs slurp up copyrighted content and regurgitate it as something "new." As a software developer who has dabbled in machine learning, it is exciting to see the field progress. But I am also an author with a large catalog of writings, and my work has been captured by at least one LLM (according to a tool that can allegedly detect these things).

Overall, current LLMs remind me of those bottom-feeder websites that do no original research--those sites that just find an article they like, lazily rewrite it, introduce a few errors, then maybe paste some baloney "sources" (which always seems to disinclude the actual original source). That mode of operation tends to be technically legal, but it's parasitic and lazy and doesn't add much value to the world.

All that aside, I tend to agree with the hypothesis that LLMs are a fad that will mostly pass. For professionals, it is really hard to get past hallucinations and the lack of citations. Imagine being a perpetual fact-checker for a very unreliable author. And laymen will probably mostly use LLMs to generate low-effort content for SEO, which will inevitably degrade the quality of the same LLMs as they breed with their own offspring. "Regression to mediocrity," as Galton put it.

>>DamnIn+IK
We use LLMs for classification. When you have limited data, LLMs work better than standard classification models like random forests. In some cases, we found LLM generated labels to be more accurate than humans.

Labeling few samples, LoRA optimizing an LLM, generating labels on millions of samples and then training a standard classifier is an easy way to get a good classifier in matter of hours/days.

Basically any task where you can handle some inaccuracy, LLMs can be a great tool. So I don't think LLMs are a fad as such.