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

Bushi Xiao; Daisy Zhe Wang; Sarvesh Soni

arxiv: 2605.28740 · v1 · pith:PRR7RENNnew · submitted 2026-05-27 · 💻 cs.CL · cs.AI

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

Bushi Xiao , Sarvesh Soni , Daisy Zhe Wang This is my paper

Pith reviewed 2026-06-29 12:19 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords uncertainty quantificationtoken-level uncertaintyclinical summarizationlarge language modelsinternal activationsreverse probingAUPRC

0 comments

The pith

Reverse Probing estimates token-level uncertainty in clinical LLM outputs by extracting signals from internal activations using pre-existing labeled summaries.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces Reverse Probing to localize uncertainty at the token level in long clinical text generated by large language models. Current uncertainty methods are built for open-domain tasks and cannot pinpoint uncertainty without expensive sampling of new outputs. Reverse Probing instead uses already-labeled summaries as probes into the model's internal state, pulling signals from four categories of activations. This yields higher detection accuracy on expert-annotated clinical datasets while cutting computation time. Readers care because clinical deployments need reliable ways to flag unsupported content before it reaches decision makers.

Core claim

Reverse Probing is the first uncertainty quantification framework specialized for clinical summarization. It estimates token-level uncertainty directly from pre-existing labeled summaries by treating the text as a probe into the model's internal state and extracting signals from four categories of internal activations. On two expert-annotated clinical datasets it outperforms eight adapted baselines on all metrics, reaching up to four times higher AUPRC while lowering inference time and computational costs. Feature analysis identifies delta energy and neighborhood context as the most consistent predictors across models.

What carries the argument

Reverse Probing, a supervised framework that extracts uncertainty signals from four categories of internal activations when the model processes pre-existing labeled summaries, without generating new samples.

If this is right

Token-level uncertainty estimates become available without repeated sampling of new model outputs.
Clinical summarization systems can localize specific unsupported content in generated text.
Uncertainty quantification requires less inference time and compute than sampling-based alternatives.
Delta energy and neighborhood context features provide consistent signals across different model sizes.
The method supplies interpretable information on how models internally respond to unsupported clinical content.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The activation-based approach could extend to other specialized long-text domains such as legal or technical writing.
The signals might be used during fine-tuning to reduce generation of unsupported spans.
Performance depends on access to high-quality labeled summaries, which may limit use in data-scarce settings.
Further checks could test correlation between these scores and actual factual errors on datasets beyond the two used here.

Load-bearing premise

Internal activations recorded while the model processes pre-existing labeled summaries supply reliable signals of uncertainty about unsupported content in new inputs.

What would settle it

Run Reverse Probing on a fresh clinical dataset containing model-generated unsupported spans and check whether its token-level scores match expert error annotations at rates clearly above the eight baselines.

Figures

Figures reproduced from arXiv: 2605.28740 by Bushi Xiao, Daisy Zhe Wang, Sarvesh Soni.

**Figure 2.** Figure 2: An example from the dataset Hallucinations [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: AUPRC across base LLMs for four selected [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: F1 vs. number of features on MIMIC-DI and [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: Top recurring features by importance across models and feature configurations. Blue cells indicate [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: The ground truth labels and uncertainty quan [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗

read the original abstract

As large language models are increasingly deployed for clinical text, ensuring they can reliably signal their own uncertainty becomes critical. Most existing uncertainty quantification (UQ) methods are designed for open-domain generation and cannot localize uncertainty at the token or span level in long clinical text. We propose Reverse Probing, the first UQ framework specialized for clinical summarization, which estimates token-level uncertainty directly from pre-existing labeled summaries. Rather than sampling new outputs, Reverse Probing treats the text as a probe into the model's internal state, extracting uncertainty signals from four categories of internal activations. We evaluate on two expert-annotated clinical datasets and outperform eight adapted baselines on all metrics, achieving up to 4 times higher AUPRC while reducing inference time and computational costs. Feature analysis reveals that delta energy and neighborhood context are the most consistent predictors across all models. This study offers interpretable insights into how models internally respond to unsupported clinical content.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Reverse Probing trains probes on internal activations from existing labeled summaries to get token-level uncertainty in clinical text and reports solid gains over baselines, but transfer to new inputs remains the untested piece.

read the letter

Reverse Probing trains supervised probes on four categories of internal activations that the model produces when it processes pre-existing labeled clinical summaries. The method then uses those probes to flag uncertain tokens on new inputs without sampling additional outputs.

The paper's main advance is specializing this style of probing to clinical summarization and showing it can localize uncertainty at the token level. It evaluates on two expert-annotated datasets, beats eight adapted baselines on every reported metric, and reaches up to 4 times higher AUPRC. It also cuts inference time and compute relative to sampling approaches. The feature analysis that singles out delta energy and neighborhood context as the most stable signals across models is a useful addition.

The practical upside is clear when labeled summaries already exist: you can train once and then apply the probes cheaply. That fits real clinical workflows better than methods that require fresh generation at test time.

The soft spot is generalizability. Clinical text is highly domain-specific in terminology and structure, so signals learned from the training summaries could reflect dataset patterns rather than intrinsic model uncertainty on unsupported content. The abstract does not describe cross-domain or out-of-distribution tests, which leaves the central transfer claim open. If those checks are absent from the full paper, the results stay tied to the two datasets used.

This work is aimed at researchers building or auditing clinical NLP systems who need token-level uncertainty without heavy extra cost. Anyone working on safe medical LLM deployment would find the method and the reported numbers worth examining.

The paper shows straightforward engineering on a real problem and engages the literature on activation-based probing. It deserves a serious referee so the details of the datasets, baselines, and any transfer experiments can be checked.

Referee Report

3 major / 2 minor

Summary. The paper introduces Reverse Probing, a supervised token-level uncertainty quantification framework for LLMs in clinical summarization. It extracts signals from four categories of internal activations while the model processes pre-existing labeled summaries, trains probes on these signals to detect uncertainty on unsupported content, and reports evaluation on two expert-annotated datasets where it outperforms eight adapted baselines on all metrics with up to 4× higher AUPRC, lower inference time, and lower computational cost. Feature analysis identifies delta energy and neighborhood context as the most consistent predictors.

Significance. If the central performance claims and generalizability hold after addressing validation gaps, the work would offer a practical, sampling-free approach to localized UQ in clinical text, which is important for safe LLM deployment in medicine. The use of existing labeled summaries for probe training is a pragmatic advantage over methods requiring new generations, and the reported feature insights could inform future interpretability research in domain-specific settings.

major comments (3)

[Abstract / Evaluation] Abstract and Evaluation: The abstract states outperformance and 4× AUPRC gains but supplies no dataset sizes, statistical tests, baseline implementation details, or ablation results. These omissions make the quantitative claims impossible to assess and are load-bearing for the central performance argument.
[Evaluation] Evaluation: No cross-domain or out-of-distribution experiments are reported despite the acknowledged domain specificity of clinical text (terminology, note structure, entity distributions). This leaves untested whether the four activation categories capture model-intrinsic uncertainty signals or merely dataset-specific patterns learned from the training summaries, which directly affects the claim of applicability to new clinical inputs.
[Methods] Methods: The four categories of internal activations used for signal extraction are referenced but not formally defined or motivated with equations or pseudocode in the visible text. Without this, it is difficult to evaluate whether the approach is reproducible or truly novel relative to existing activation-based probing techniques.

minor comments (2)

[Abstract] The abstract mentions 'reducing inference time and computational costs' but does not quantify the savings or compare wall-clock times against the baselines; a table with these metrics would strengthen the efficiency claim.
[Evaluation] Clarify the exact token-level labeling scheme used for the expert-annotated datasets (e.g., how unsupported spans are marked) to allow readers to judge label quality.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment point by point below, indicating where we agree and plan revisions to strengthen the manuscript.

read point-by-point responses

Referee: [Abstract / Evaluation] Abstract and Evaluation: The abstract states outperformance and 4× AUPRC gains but supplies no dataset sizes, statistical tests, baseline implementation details, or ablation results. These omissions make the quantitative claims impossible to assess and are load-bearing for the central performance argument.

Authors: We agree that the abstract's brevity limits the inclusion of supporting details. The full manuscript reports the sizes of the two expert-annotated datasets, baseline implementation specifics in the Evaluation section, ablation studies, and statistical significance testing for the reported gains. We will revise the abstract to incorporate dataset sizes and a brief mention of statistical support for the performance improvements, within length constraints. revision: partial
Referee: [Evaluation] Evaluation: No cross-domain or out-of-distribution experiments are reported despite the acknowledged domain specificity of clinical text (terminology, note structure, entity distributions). This leaves untested whether the four activation categories capture model-intrinsic uncertainty signals or merely dataset-specific patterns learned from the training summaries, which directly affects the claim of applicability to new clinical inputs.

Authors: This is a fair and important point regarding generalizability. Our experiments use two distinct expert-annotated clinical datasets to demonstrate consistency, but we did not conduct explicit cross-domain or OOD evaluations. We will add a limitations discussion acknowledging this gap and the risk of dataset-specific patterns, while noting that the activation categories target model-intrinsic signals; we will also outline plans for future broader validation. revision: partial
Referee: [Methods] Methods: The four categories of internal activations used for signal extraction are referenced but not formally defined or motivated with equations or pseudocode in the visible text. Without this, it is difficult to evaluate whether the approach is reproducible or truly novel relative to existing activation-based probing techniques.

Authors: We thank the referee for highlighting this clarity issue. The full Methods section formally defines the four activation categories with equations, motivates their selection, and includes pseudocode for the signal extraction and probing process. We will revise the presentation to make these definitions and motivations more prominent and explicit to support reproducibility and differentiation from prior work. revision: yes

Circularity Check

0 steps flagged

No circularity detected in derivation chain

full rationale

The paper introduces Reverse Probing as a supervised framework that trains on internal activations extracted while processing pre-existing labeled summaries to predict token-level uncertainty. Evaluation is performed on two expert-annotated clinical datasets against eight baselines, with reported AUPRC gains. No equations, mathematical derivations, fitted parameters renamed as predictions, or self-citation chains are present in the provided text that would reduce the central claim to its inputs by construction. The approach is a standard feature-based supervised method whose validity rests on empirical performance rather than definitional equivalence or imported uniqueness theorems.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no equations, training details, or modeling choices; free parameters, axioms, and invented entities cannot be identified.

pith-pipeline@v0.9.1-grok · 5690 in / 1140 out tokens · 33411 ms · 2026-06-29T12:19:20.988736+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

5 extracted references · 5 canonical work pages · 4 internal anchors

[1]

The Llama 3 Herd of Models

A meta-evaluation of faithfulness metrics for long-form hospital-course summarization. InMa- chine Learning for Healthcare Conference, pages 2–30. PMLR. Olivier Bodenreider. 2004. The unified medical lan- guage system (UMLS): integrating biomedical ter- minology.Nucleic Acids Research, 32(Database issue):D267–D270. Tianqi Chen and Carlos Guestrin. 2016. X...

work page internal anchor Pith review Pith/arXiv arXiv 2004
[2]

Mistral 7B

Medical Expert Annotations of Unsupported Facts in Doctor-Written and LLM-Generated Patient Summaries.PhysioNet. Version 1.0.1. Stefan Hegselmann, Zejiang Shen, Florian Gierse, Mon- ica Agrawal, David Sontag, and Xiaoyi Jiang. 2024. A data-centric approach to generate faithful and high quality patient summaries with large language mod- els. InConference o...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[3]

Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs

Lightgbm: a highly efficient gradient boosting decision tree. InProceedings of the 31st Interna- tional Conference on Neural Information Processing Systems, NIPS’17, page 3149–3157, Red Hook, NY , USA. Curran Associates Inc. Jannik Kossen, Jiatong Han, Muhammed Razzak, Lisa Schut, Shreshth Malik, and Yarin Gal. 2024. Se- mantic entropy probes: Robust and ...

work page internal anchor Pith review Pith/arXiv arXiv 2024
[4]

Yingzhou Lu, Tianyi Chen, Nan Hao, Capucine Van Rechem, Jintai Chen, and Tianfan Fu

Uncertainty estimation and quantification for llms: A simple supervised approach.Preprint, arXiv:2404.15993. Yingzhou Lu, Tianyi Chen, Nan Hao, Capucine Van Rechem, Jintai Chen, and Tianfan Fu. 2024. Un- certainty quantification and interpretability for clin- ical trial approval prediction.Health Data Science, 4:0126. Scott M Lundberg and Su-In Lee. 2017....

work page arXiv 2024
[5]

Efficient Hallucination Detection for LLMs Using Uncertainty-Aware Attention Heads

Uncertainty-aware attention heads: Efficient unsupervised uncertainty quantification for LLMs. arXiv preprint arXiv:2505.20045. Chenyu Wang, Weichao Zhou, Shantanu Ghosh, Kay- han Batmanghelich, and Wenchao Li. 2025. Seman- tic consistency-based uncertainty quantification for factuality in radiology report generation. InFind- ings of the Association for C...

work page internal anchor Pith review Pith/arXiv arXiv 2025

[1] [1]

The Llama 3 Herd of Models

A meta-evaluation of faithfulness metrics for long-form hospital-course summarization. InMa- chine Learning for Healthcare Conference, pages 2–30. PMLR. Olivier Bodenreider. 2004. The unified medical lan- guage system (UMLS): integrating biomedical ter- minology.Nucleic Acids Research, 32(Database issue):D267–D270. Tianqi Chen and Carlos Guestrin. 2016. X...

work page internal anchor Pith review Pith/arXiv arXiv 2004

[2] [2]

Mistral 7B

Medical Expert Annotations of Unsupported Facts in Doctor-Written and LLM-Generated Patient Summaries.PhysioNet. Version 1.0.1. Stefan Hegselmann, Zejiang Shen, Florian Gierse, Mon- ica Agrawal, David Sontag, and Xiaoyi Jiang. 2024. A data-centric approach to generate faithful and high quality patient summaries with large language mod- els. InConference o...

work page internal anchor Pith review Pith/arXiv arXiv 2024

[3] [3]

Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs

Lightgbm: a highly efficient gradient boosting decision tree. InProceedings of the 31st Interna- tional Conference on Neural Information Processing Systems, NIPS’17, page 3149–3157, Red Hook, NY , USA. Curran Associates Inc. Jannik Kossen, Jiatong Han, Muhammed Razzak, Lisa Schut, Shreshth Malik, and Yarin Gal. 2024. Se- mantic entropy probes: Robust and ...

work page internal anchor Pith review Pith/arXiv arXiv 2024

[4] [4]

Yingzhou Lu, Tianyi Chen, Nan Hao, Capucine Van Rechem, Jintai Chen, and Tianfan Fu

Uncertainty estimation and quantification for llms: A simple supervised approach.Preprint, arXiv:2404.15993. Yingzhou Lu, Tianyi Chen, Nan Hao, Capucine Van Rechem, Jintai Chen, and Tianfan Fu. 2024. Un- certainty quantification and interpretability for clin- ical trial approval prediction.Health Data Science, 4:0126. Scott M Lundberg and Su-In Lee. 2017....

work page arXiv 2024

[5] [5]

Efficient Hallucination Detection for LLMs Using Uncertainty-Aware Attention Heads

Uncertainty-aware attention heads: Efficient unsupervised uncertainty quantification for LLMs. arXiv preprint arXiv:2505.20045. Chenyu Wang, Weichao Zhou, Shantanu Ghosh, Kay- han Batmanghelich, and Wenchao Li. 2025. Seman- tic consistency-based uncertainty quantification for factuality in radiology report generation. InFind- ings of the Association for C...

work page internal anchor Pith review Pith/arXiv arXiv 2025