I'd adjust that statement - If an LLM can solve a complex problem 50% of the time and I can evaluate correctness of the output, then that is still very valuable. I've seen too many people blindly pass on LLM output - for a short while it was a trend in the scientific literature to have LLMs evaluate output of other LLMs? Who knows how correct that was. Luckily that has ended.
I misread this the first time and realised both interpretations are happening. I've seen people copy-paste out of ChatGPT without reading, and I've seen people "pass on" or reject content simply because it has been AI generated.
That said, I do think there are lots of problems where verification is easier than doing the task itself, especially in computer science. I think it is easier to list tasks that aren't easier to verify than to do from scratch actually. Security is one major one.
Highly reliable. So much so that is basically how modern LLMs work internally. Also speaking from personal experience in the projects I work on, it is the chief way to counteract hallucination, poisoned context windows, and scaling beyond the interaction limit.
LLMs evaluating LLM output works surprisingly well.
What do you mean it ended? I still see tons of NLP papers with this methodology.