Calibrating Large Language Models with Concept Activation Vectors for Medical QA

We propose a novel framework for calibrating LLM uncertainty through Concept Activation Vectors. This improves the safety and calibration of LLMs in high-stakes medical decision making.

Recommended citation: Qin, J. et al. (2024). "Calibrating Large Language Models with Concept Activation Vectors for Medical QA."