Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values 事件

Localizing Input Uncertainty Quantification for Large Language Models via Shapley Values arXiv:2605.28170v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly integrated into high-stakes decision-making, the ability to reliably quantify uncertainty has become a critical requirement for safety and trust. However, current uncertainty quantification methods primarily operate at the output level, often failing to distinguish whether uncertainty arises from the model's lac