I’m with the people pushing back on the “confidence scores” framing, but I think the deeper issue is that we’re still stuck in the wrong mental model.

It’s tempting to think of a language model as a shallow search engine that happens to output text, but that metaphor doesn’t actually match what’s happening under the hood. A model doesn’t “know” facts or measure uncertainty in a Bayesian sense. All it really does is traverse a high‑dimensional statistical manifold of language usage, trying to produce the most plausible continuation.

That’s why a confidence number that looks sensible can still be as made up as the underlying output, because both are just sequences of tokens tied to trained patterns, not anchored truth values. If you want truth, you want something that couples probability distributions to real world evidence sources and flags when it doesn’t have enough grounding to answer, ideally with explicit uncertainty, not hand‑waviness.

People talk about hallucination like it’s a bug that can be patched at the surface level. I think it’s actually a feature of the architecture we’re using: generating plausible continuations by design. You have to change the shape of the model or augment it with tooling that directly references verified knowledge sources before you get reliability that matters.

How do you know the confidence scores are not hallucinated as well?

LLMs fail at causal accuracy. It's a fundamental problem with how they work.

Asking an LLM to give itself a «confidence score» is like asking a teenager to grade his own exam. I LLMs doesn’t «feel» uncertainty and confidence like we do.