Reverse Probing: Supervised Token-level Uncertainty Quantification for Large Language Models in Clinical Text

Large language models are being increasingly used in clinical text analysis, making it crucial for them to accurately quantify their own uncertainty. Existing uncertainty quantification methods are limited, as they are designed for open-domain generation and fail to pinpoint uncertainty at the token or span level in lengthy clinical texts. Researchers have introduced Reverse Probing, a novel framework specifically designed for clinical text, which enables token-level uncertainty quantification¹. This approach addresses the limitations of existing methods, allowing large language models to reliably signal their uncertainty in clinical text analysis. By localizing uncertainty, Reverse Probing can help mitigate potential errors and improve the overall reliability of clinical text analysis. This development matters to practitioners, as it can enhance the trustworthiness of AI-driven clinical decision-making.