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Comment by loose-cannon

3 days ago

Dude, these two things are not at all analogous:

1. Asking a model why it did a certain thing, and

2. Expecting a human to say which neuron fired in their response.

Even asking a human being why they did a certain thing is questionable. The research on choice blindness seems like a pretty definitive debunking of post-hoc rationalization:

https://en.wikipedia.org/wiki/Introspection_illusion#Choice_...

  • I'm not sure what point you're trying to make. In science and engineering, being able to provide justification is a core skill. The comparison we should be making is against the human practitioners who are trained in their fields. There will always be a distribution of ability. Saying that there's evidence that people are capable of providing post-hoc rationalization doesn't say anything about the ability of experts to produce well thought out responses (in their respective fields) that don't immediately fall apart under scrutiny.

    • Structured thinking and deliberation are indeed important, but you can also make LLMs do structured "thinking" if you work hard enough, and generate quite plausible reasoned arguments with valid real-world results, and you can get them to write down their working as they go. But as research has shown, it's not "true" thinking, just pattern matching at a higher level, and eventually runs out of steam.[0]

      But you only have to drill down a couple more layers and you are back in the void again; do you have any proof that your own thinking, no matter how structured and accurate, is anything other than pattern-matching at a sufficiently much higher level at which you are incapable of seeing it as such?

      I think we will be finding some very interesting things out soon using the combination of LLMs and theorem provers, as demonstrated by Terence Tao's recent work.[1]

      A cheetah is not a motorbike is not an aircraft is not a rocket.

      [0] https://arxiv.org/abs/2506.06941

      [1] https://arxiv.org/abs/2603.12744