It's not like these are new issues. They're the same ones we've experienced since the introduction of these tools. And yet the focus has always been to throw more data and compute at the problem, and optimize for fancy benchmarks, instead of addressing these fundamental problems. Worse still, whenever they're brought up users are blamed for "holding it wrong", or for misunderstanding how the tools work. I don't care. An "artificial intelligence" shouldn't be plagued by these issues.
My feelings exactly, but you’re articulating it better than I typically do ha
Exactly, that's why not verifying the output is even less defensible now than it ever has been - especially for professional scientists who are responsible for the quality of their own work.
I’m not saying that isn’t what has to be done, but it kind of clashes with the whole “this will make you more productive” argument if you ask me
LLM’s simply aren’t good enough for all the use cases some people insist they are. They’re powerful tools that have been far too broadly applied and there’s too much money and too many reputations being put on the line to acknowledge the obvious limitations. Frankly I’m sick of it.
I had somebody on HN a few months ago insist to me that because we value art and fiction, LLM’s being wrong when we need them to be correct (in ways that are also not always easy to identify) was desirable. I don’t even know what to do with that kind of logic other than chalk it up as trolling. I don’t want my computer to trick me into false solutions.