Okay. And the fact that LLMs routinely make up crap that doesn't exist but sounds plausible, and the fact that this appears to be a fundamental problem with LLMs, this doesn't give you any pause on your hype train? Genuine question, how do you reconcile this?

> I really wish OpenAI gave us the whole thought process verbatim, as I'm kind of curious where those "thoughts" come from and what happens to them.

Don't see what you mean by this; there's no such thing as "thoughts" of an LLM, and if you mean the feature marketers called chain-of-thought, it's yet another instance of LLMs making shit up, so.

> And the fact that LLMs routinely make up crap that doesn't exist but sounds plausible, and the fact that this appears to be a fundamental problem with LLMs, this doesn't give you any pause on your hype train? Genuine question, how do you reconcile this?

Simply. Because the same is the case with humans. Mostly for the same reasons.

(Are humans overhyped? Maybe?)

The LLM hype train isn't about them being more accurate or faster than what came before - it comes from them being able to understand what you mean. It's a whole new category of software - programs that can process natural language like humans would; a powerful side effect that took the world by surprise is, that making LLMs better at working with natural language implicitly turns them into general-purpose problem solvers.

> Don't see what you mean by this; there's no such thing as "thoughts" of an LLM, and if you mean the feature marketers called chain-of-thought, it's yet another instance of LLMs making shit up, so.

"Chain-of-thought" is so 2024; current models don't need to be told to "think step by step", they're post-trained to first generate a stream of intermediary tokens not meant as "actual answer", before continuing with the "actual answer". You can call it however you like; however both research literature and vendors settled on calling it "thinking" or "reasoning". Treat them as terms of art, if that helps.