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

Researchers have proposed a new framework called Reverse Probing to quantify uncertainty in clinical text generated by large language models, reporting performance gains over existing methods on expert-annotated data [1][2]. The framework, detailed in a paper submitted to arXiv on 27 May 2026, is described as the first uncertainty quantification (UQ) method specialized for clinical summarization [1][2]. Unlike conventional UQ approaches designed for open-domain generation, Reverse Probing estimates token-level uncertainty directly from pre-existing labeled summaries without sampling new outputs [1][2]. It extracts uncertainty signals from four categories of internal model activations [1][2]. Large language models, which are neural networks trained on vast text corpora, underpin modern chatbots and summarization tools but can produce unreliable output when training data is biased or inaccurate [3]. The challenge is acute in clinical settings, where unsupported content carries direct risk [1][2]. Reverse Probing was evaluated on two expert-annotated clinical datasets and outperformed eight adapted baselines across all metrics, achieving up to 4 times higher area under the precision-recall curve (AUPRC) while reducing inference time and computational costs [1][2]. Feature analysis showed that delta energy and neighborhood context were the most consistent predictors of uncertainty across all models tested [1][2]. High-quality labeled datasets, such as the expert-annotated clinical collections used in the study, are typically difficult and expensive to produce because of the time required for expert labeling [4]. The paper’s authors note that the method offers interpretable insights into how models internally respond to unsupported clinical content [1][2]. Code and data associated with the article are linked through platforms including Hugging Face and alphaXiv [1].