Verbalized confidence: make an LLM state how sure it is, then check if 90% really means 90%

1Key Takeaways

• Ask a model a question and it answers with the same flat certainty whether it knows the capital of Australia or is guessing a coin-flip.
• Verbalized confidence fixes half of that: you ask it to attach an explicit self-estimate — "Answer: Canberra, Confidence: 90%." That single extra line turns an opaque guess into a signal you can gate, route, or abstain on.
• The other half — the part everyone skips — is checking whether the number is honest.
• Eliciting the number is one system instruction The….