Aligning Language Models with Selective Prediction

Researchers have introduced a new training framework designed to improve the reliability of large language models by enabling them to selectively decline answering prompts when they lack sufficient confidence. This method helps models identify and abstain from generating potentially inaccurate outputs, reducing the risk of errors in high-stakes decision-making environments.