alt Hacker NewsYou're misunderstanding my assertion.
When you give prompt P to model M, when your goal is for the model to actually execute those instructions, the model will be in state S.
When you give the same prompt to the same model, when your goal is for the model to introspect on those instructions, the model is still in state S. It's the exact same input, and therefore the exact same model state as the starting point.
Introspection-mode state only diverges from execution-mode state at the point at which you subsequently give it an introspection command.
At that point, asking the model to e.g. note any ambiguities about the task at hand is exactly equivalent to asking it to evaluate any input, and there is overwhelming evidence that frontier models do this very well, and have for some time.
Asking the model, while it's in state S, to introspect and surface any points of confusion or ambiguities it's experiencing about what it's being asked to do, is an extremely valuable part of the prompt engineering toolkit.
I didn't, and don't, assert that "asking the model if the instructions are good" is a replacement for evals – that's a strawman argument you seem to be constructing on your own and misattributing to me.
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At that point, asking the model to e.g. note any ambiguities about the task at hand is exactly equivalent to asking it to evaluate any input
Prompt P at state S leads to a new state SP'. The "common jumping off point" you describe is effectively useless, because we instantly diverge from it by using different prompts.
And even if it weren't useless for that reason, LLMs don't "query" their "state" in the way that humans reflect on their state of mind.
The idea that hallucinations are somehow less likely because you're asking meta-questions about LLM output is completely without basis
Nicely put. I haven't seen anyone say that the introspection abilities of LLMs are up to much, but claiming that it's completely impossible to get a glimpse behind the curtain is untrue.