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

Researchers introduce Reverse Probing, a novel uncertainty quantification framework designed specifically for clinical LLMs that estimates token-level confidence directly from existing summaries rather than sampling new outputs. The method achieves significant performance improvements on clinical datasets while reducing computational costs, advancing the critical goal of making AI systems safer for healthcare applications.

Reverse Probing addresses a fundamental challenge in deploying large language models within healthcare systems: the inability to reliably communicate when the model is uncertain about specific tokens or spans in clinical text. Traditional uncertainty quantification methods developed for open-domain language generation lack the precision needed for clinical applications where errors carry serious consequences. This research introduces a supervised learning approach that extracts uncertainty signals from internal model activations by treating clinical text as a probe into the model's decision-making process.

The healthcare AI sector has struggled with transparency and reliability concerns, particularly when LLMs generate medical summaries or clinical notes. Existing UQ methods cannot pinpoint uncertainty at fine-grained levels necessary for clinicians to identify potentially problematic AI-generated content. Reverse Probing fills this gap by analyzing four categories of internal activations, enabling the framework to achieve up to 4x higher AUPRC compared to adapted baselines while simultaneously reducing inference time and computational overhead.

For healthcare institutions and AI developers, this work has immediate practical implications. The ability to localize uncertainty at the token level allows clinicians to quickly identify sections of AI-generated clinical text requiring human review, reducing manual verification burden without sacrificing safety. The framework's efficiency gains make deployment more feasible in resource-constrained healthcare settings. Feature analysis showing that delta energy and neighborhood context are consistent uncertainty predictors provides interpretable insights that could inform future model improvements across multiple architectures and datasets.

• →Reverse Probing achieves up to 4x higher AUPRC than baseline methods while reducing computational costs for clinical LLM uncertainty quantification.
• →The framework enables token-level uncertainty localization in long clinical texts, allowing clinicians to identify problematic AI outputs efficiently.
• →Internal activation analysis reveals delta energy and neighborhood context as the most consistent uncertainty predictors across different models.
• →Expert-annotated clinical datasets demonstrate the method's superiority on specialized healthcare applications beyond general-domain language generation tasks.
• →The supervised learning approach leverages existing labeled summaries rather than requiring new model sampling, improving practical deployment feasibility.