AIBullisharXiv – CS AI · Mar 36/103
🧠
Calibrating Verbalized Confidence with Self-Generated Distractors
Researchers introduce DINCO (Distractor-Normalized Coherence), a method to improve confidence calibration in large language models by using self-generated alternative claims to reduce overconfidence bias. The approach addresses LLM suggestibility issues that cause models to express high confidence on low-accuracy outputs, potentially improving AI safety and trustworthiness.