The Knowing-Saying Gap: When Probes See Errors that Confidence Misses
New research exposes the gap between model internal monitoring and confidence, complicating AI reliability efforts.
Linear probes detect corrupted context in language models with near-perfect accuracy, yet this does not translate into reliable failure prediction. The result is a dissociation with direct implications for deployment monitoring. Across multi-hop arithmetic chains, probes that det...
ArXiv CS.AI
Source
Generated insight by OnAirToday AI
This research reveals a critical dissociation between an LLM's internal representation of errors and its reported confidence scores, undermining reliance on simple monitoring tools. It highlights that robust AI safety requires model-specific, context-aware intervention strategies rather than generic, one-size-fits-all fail-safes.
Comments
Keep comments relevant and respectful. No links, advertising, self-promotion, or spam.