The Confidence Gap in LLMs
Measuring and addressing overconfidence in large language models
This research evaluates how well large language models (LLMs) calibrate their confidence levels, revealing significant overconfidence issues that pose risks in high-stakes applications.
Key findings:
- LLMs demonstrate widespread overconfidence across different tasks and question types
- Distractors significantly impact model confidence and calibration
- Model size affects confidence calibration, with implications for model selection
- Multiple-choice formats can reveal miscalibration patterns not evident in other evaluation methods
Why it matters for Security: Improper confidence calibration creates security risks in high-stakes scenarios where model outputs inform critical decisions. Understanding these patterns helps develop more reliable AI systems with appropriate uncertainty expression.