Research3d ago
SelfDoubt Quantifies LLM Uncertainty
SELFDOUBT introduces the Hedge-to-Verify Ratio as a single‑pass uncertainty metric. The method pulls behavioral cues from a model’s reasoning trace, sidestepping the need for logits or token probabilities. It works with proprietary APIs that hide internal signals, giving practitioners a dependable uncertainty estimate. This enables safer deployment of reasoning LLMs.