Reverse Probing: Supervised Token-level Uncertainty Quantification for Large Language Models in Clinical Text
Bushi Xiao, Sarvesh Soni, Daisy Zhe Wang
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Key claim
Reverse Probing significantly improves uncertainty quantification in clinical text.
This paper presents Reverse Probing, a new framework for quantifying uncertainty in clinical text summarization. It achieves significant improvements in performance metrics, including up to 4 times higher AUPRC, while also reducing computational costs. The findings provide valuable insights into model behavior regarding clinical content.
In plain English
This paper presents Reverse Probing, a new framework for quantifying uncertainty in clinical text summarization. It achieves significant improvements in performance metrics, including up to 4 times higher AUPRC, while also reducing computational costs. The findings provide valuable insights into model behavior regarding clinical content.
The proposed Reverse Probing framework introduces a novel approach to uncertainty quantification specifically tailored for clinical summarization.
The evaluation on expert-annotated datasets and comparison against multiple baselines supports the claims made in the study.
Deep reliability assessment
The methodology supports a supervised token-level classifier that can use frozen LLM internal activations, with and without clinical evidence, to identify unsupported spans in discharge-summary datasets. The stronger claims are that this is general clinical uncertainty quantification and model self-assessment, since the evidence shown is limited to two annotated discharge-summary datasets, mostly 7-8B Mistral/Llama-style models, and labels of unsupported content rather than independently validated subjective uncertainty.
Reproducibility
Code: no repository mentioned. Dataset: yes, the paper uses Hallucinations-MIMIC-DI and Hallucinations-Generated-DI, derived from MIMIC-IV-Note on PhysioNet, but access requires credentialed registration, CITI training, and a data use agreement.
Discussion questions
- 1.Does Reverse Probing really measure the model's uncertainty, or is it learning a supervised detector for unsupported clinical facts from activation patterns correlated with the annotation scheme?
- 2.For builders deploying clinical summarization, is the added complexity of extracting hidden states and training a token-level classifier justified compared with simpler retrieval-grounded citation or claim-verification pipelines?
- 3.What result would falsify the core claim: poor transfer to a new hospital note type, failure on a held-out model family, or cases where unsupported tokens still show strong BHC anchoring in the internal representations?
Key figure
Figure 1 shows the Brief Hospital Course and clinical summary being fed into a frozen LLM, from which four categories of internal features are extracted and passed to a supervised classifier that predicts token-level uncertainty.