Confidence Before Answering: A Paradigm Shift for Efficient LLM Uncertainty Estimation 事件

Confidence Before Answering: A Paradigm Shift for Efficient LLM Uncertainty Estimation arXiv:2603.05881v2 Announce Type: replace Abstract: Reliable deployment of large language models (LLMs) requires accurate uncertainty estimation. Existing methods are predominantly answer-first, producing confidence only after generating an answer, which measure the correctness of a specific response and limits practical usability. We study a confidence-first paradigm, where the model outputs its confidence b