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

Problem
This paper addresses the gap in uncertainty quantification (UQ) methods for large language models (LLMs) applied to clinical text. Existing UQ techniques primarily focus on open-domain generation and lack the capability to localize uncertainty at the token or span level within lengthy clinical documents. The authors propose Reverse Probing, a novel framework specifically designed for clinical summarization, which is particularly relevant given the increasing deployment of LLMs in healthcare settings. This work is presented as a preprint and has not yet undergone peer review.

Method
Reverse Probing operates by leveraging pre-existing labeled summaries to estimate token-level uncertainty. Instead of generating new outputs, it treats the clinical text as a probe into the model’s internal state, extracting uncertainty signals from four categories of internal activations. The architecture is based on existing LLMs, but the specific model used is not disclosed. The authors employ a supervised learning approach, utilizing labeled data from clinical datasets to train the model. The loss function is not explicitly detailed, but the focus on token-level uncertainty suggests a potential use of cross-entropy or similar metrics. The training compute requirements are not specified, but the authors claim reduced inference time and computational costs compared to baseline methods.

Results
The proposed method was evaluated on two expert-annotated clinical datasets, where it outperformed eight adapted baselines across all metrics. Notably, Reverse Probing achieved up to 4 times higher Area Under the Precision-Recall Curve (AUPRC) compared to the best-performing baseline. The results indicate significant improvements in UQ for clinical text summarization, although specific baseline names and metrics beyond AUPRC are not provided in the summary.

Limitations
The authors acknowledge that their approach is specialized for clinical summarization and may not generalize to other domains or tasks outside of clinical text. Additionally, the reliance on pre-existing labeled summaries may limit the applicability of Reverse Probing in scenarios where such data is scarce. The paper does not discuss potential biases in the datasets used for evaluation, which could affect the generalizability of the findings. Furthermore, the interpretability of the extracted uncertainty signals, while mentioned, is not deeply explored.

Why it matters
This work has significant implications for the deployment of LLMs in clinical settings, where understanding model uncertainty is crucial for decision-making. By providing a method for token-level UQ, Reverse Probing enhances the interpretability of model outputs, allowing clinicians to better assess the reliability of generated summaries. The insights gained from feature analysis, particularly regarding delta energy and neighborhood context as predictors, could inform future research on model interpretability and robustness in healthcare applications. This framework may pave the way for more reliable AI systems in clinical environments, ultimately improving patient outcomes.

Authors: Bushi Xiao, Sarvesh Soni, Daisy Zhe Wang
Source: arXiv:2605.28740
URL: https://arxiv.org/abs/2605.28740v1