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arxiv: 2601.16397 · v2 · submitted 2026-01-23 · 💻 cs.CL · cs.AI

From Attribution to Abstention: Training-Free Attention-Based Auditing for Clinical Summarization

Qianqi Yan , Huy Nguyen , Sumana Srivatsa , Hari Bandi , Xin Eric Wang , Krishnaram Kenthapadi This is my paper

Pith reviewed 2026-05-16 12:33 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords clinical summarizationattention-based auditingsource attributionhallucination detectionmultimodal LLMstraining-freeabstention
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The pith

ClinTrace extracts source attributions and groundedness scores directly from decoder attention in medical MLLMs to audit clinical summaries without training.

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

ClinTrace is a training-free framework that pulls two signals from the decoder attention weights already produced by any transformer-based multimodal LLM during generation: fine-grained links from each output sentence to supporting input text spans or images, and per-sentence groundedness scores that flag poorly supported claims. On doctor-patient dialogue summarization and radiology report summarization, using both a general MLLM and a medical-finetuned model, the method reaches over 92 percent text F1 for attribution on radiology and 88 percent on dialogue while delivering 0.77 AUROC for hallucination detection. Abstaining on the least grounded 20 percent of output sentences raises faithfulness from 61.7 percent to 72.6 percent at zero added inference cost, and medical finetuning visibly improves how well attention encodes semantic support.

Core claim

Decoder attention tensors produced during generation already contain enough structure to compute sentence-level source attributions and groundedness scores in one forward pass, yielding high-accuracy auditing on both general and medically adapted MLLMs for clinical summarization tasks.

What carries the argument

Decoder attention weights aggregated across layers and heads to link each generated sentence to its supporting input spans or images and to derive groundedness scores.

If this is right

  • Source attribution becomes available at no extra cost during normal generation.
  • Abstaining on low-groundedness sentences measurably raises summary faithfulness.
  • Medical finetuning makes attention patterns more useful for self-auditing.
  • The same attention tensors serve both attribution and hallucination detection.

Where Pith is reading between the lines

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

  • The approach may transfer to other fine-tuned domains where attention patterns become more semantically organized.
  • Attention-derived groundedness could be combined with embedding-based confidence scores for further gains.
  • The method implies that domain adaptation improves the internal traceability of generated claims.

Load-bearing premise

Decoder attention weights in the tested MLLMs encode reliable semantic links between output statements and source material even after medical finetuning.

What would settle it

Human experts independently label supporting source spans and groundedness for a held-out set of generated clinical summaries and compare those labels to the attention-derived attributions and scores.

Figures

Figures reproduced from arXiv: 2601.16397 by Hari Bandi, Huy Nguyen, Krishnaram Kenthapadi, Qianqi Yan, Sumana Srivatsa, Xin Eric Wang.

Figure 1
Figure 1. Figure 1: Overview of our proposed framework for inference-time source attribution in clinical [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Ablation on text-only attribution. (A) Majority vs. Max (best τ per k): majority voting yields 60-point higher Macro-F1 and far stronger exact match than max, which collapses to near￾random performance. (B) Effect of k under majority voting: attribution quality peaks around τ = 0.2, with k = 3 providing the best balance between robustness and stability. (C) Fine-grained τ sweep for k = 3, majority shows th… view at source ↗
read the original abstract

Deploying multimodal large language models (MLLMs) for clinical summarization demands not only fluent generation but also transparency about where each statement originates-and a mechanism to flag when statements lack evidential support. We present ClinTrace, a training-free framework that extracts two clinically useful signals from the decoder attention weights that every transformer-based MLLM already produces during generation: (i) fine-grained source attributions linking each output sentence to supporting text spans or images, and (ii) per-sentence groundedness scores that identify poorly supported claims as candidate hallucinations. Both signals are derived from the same attention tensors in a single pass, requiring no retraining, no auxiliary models, and no additional inference cost. We evaluate on two clinical summarization tasks: doctor-patient dialogue summarization (CliConSummation) and radiology report summarization (MIMIC-CXR) using a general-purpose MLLM (Qwen3-8B) and a medical-finetuned model (HuatuoGPT-Vision-7B). For source attribution, ClinTrace achieves over 92% text F1 on radiology and 88% on dialogue summarization, substantially outperforming embedding-based and self-attribution baselines. For hallucination detection, groundedness scores achieve 0.77 AUROC with the medical-finetuned model: competitive with embedding-based confidence at zero additional cost, and enable an abstention mechanism that improves faithfulness from 61.7% to 72.6% by withholding the least: grounded 20% of output for clinician review. Notably, medical finetuning substantially improves the reliability of attention-based hallucination detection, suggesting that domain adaptation produces more semantically structured attention patterns amenable to self-auditing.

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.

Referee Report

2 major / 2 minor

Summary. The paper presents ClinTrace, a training-free framework that extracts source attributions and groundedness scores directly from decoder attention weights in multimodal LLMs for clinical summarization. Evaluated on doctor-patient dialogue summarization (CliConSummation) and radiology report summarization (MIMIC-CXR) using Qwen3-8B and HuatuoGPT-Vision-7B, it reports over 92% text F1 for source attribution on radiology and 88% on dialogue, outperforming baselines, and 0.77 AUROC for hallucination detection with the medical model, enabling abstention that boosts faithfulness from 61.7% to 72.6%.

Significance. If the attention-derived signals prove to encode reliable semantic grounding, the framework supplies a zero-cost auditing tool for clinical MLLM deployment that requires no retraining or auxiliary models. The reported gains from medical finetuning on attention structure and the abstention-based faithfulness lift (61.7% to 72.6%) would be practically useful for safety-critical summarization.

major comments (2)
  1. [Methods] Methods section: the precise aggregation rule or formula used to derive per-sentence groundedness scores from decoder attention tensors is not stated, rendering the 0.77 AUROC claim impossible to reproduce or stress-test against alternative normalizations.
  2. [Experiments] Experiments section: no ablation with randomized or position-shuffled attention baselines is reported, leaving open the possibility that the >92% text F1 on MIMIC-CXR and 88% on dialogue summarization arise from lexical or positional artifacts rather than semantic source links.
minor comments (2)
  1. [Abstract] Abstract and §4: the exact implementations of the embedding-based and self-attribution baselines should be specified (e.g., embedding model, similarity metric, and threshold selection) to permit direct replication.
  2. [Evaluation] Evaluation: add a short error analysis or qualitative examples of attribution failures on the dialogue task to contextualize the 88% F1 score.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment point by point below and will revise the manuscript to improve clarity and experimental rigor where needed.

read point-by-point responses

  1. Referee: [Methods] Methods section: the precise aggregation rule or formula used to derive per-sentence groundedness scores from decoder attention tensors is not stated, rendering the 0.77 AUROC claim impossible to reproduce or stress-test against alternative normalizations.

    Authors: We agree that the precise aggregation rule was not stated explicitly enough in the Methods section. The groundedness score is obtained by averaging the decoder attention weights from tokens in each output sentence to the corresponding input source tokens (text spans or image patches) and normalizing by the total attention mass per sentence, but the exact formula was omitted for brevity. We will add the full mathematical definition in the revised Methods section to enable reproduction and testing of alternative normalizations. revision: yes

  2. Referee: [Experiments] Experiments section: no ablation with randomized or position-shuffled attention baselines is reported, leaving open the possibility that the >92% text F1 on MIMIC-CXR and 88% on dialogue summarization arise from lexical or positional artifacts rather than semantic source links.

    Authors: We acknowledge that an ablation with randomized or position-shuffled attention would further rule out lexical or positional artifacts. Our existing embedding-based and self-attribution baselines already provide some control for lexical overlap, but we did not include randomized attention controls. We will add this ablation study to the revised Experiments section, randomizing attention weights while preserving row/column sums, to confirm that the reported F1 scores reflect semantic grounding. revision: yes

Circularity Check

0 steps flagged

No significant circularity: ClinTrace derives attribution and groundedness directly from unmodified decoder attention tensors

full rationale

The paper's central derivation extracts source attributions and per-sentence groundedness scores in a single forward pass from the decoder attention tensors already produced by the base MLLMs (Qwen3-8B and HuatuoGPT-Vision-7B). No parameters are fitted to the target F1 or AUROC metrics, no quantities are redefined in terms of the evaluation outcomes, and no self-citation chain is invoked to justify uniqueness or force the method. The reported performance numbers are computed against external human-annotated ground truth on held-out clinical datasets, leaving the derivation self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that attention weights encode source and groundedness information after medical finetuning; no free parameters are introduced and no new physical or mathematical entities are postulated.

axioms (1)
  • domain assumption Decoder attention weights in transformer-based MLLMs encode semantically meaningful source links for generated clinical statements
    Invoked to justify using attention tensors for attribution and groundedness scoring without additional training
invented entities (1)
  • ClinTrace framework no independent evidence
    purpose: Extracts attribution and groundedness signals from existing attention tensors
    New named method introduced by the authors; no independent evidence outside the paper

pith-pipeline@v0.9.0 · 5636 in / 1385 out tokens · 29266 ms · 2026-05-16T12:33:54.270856+00:00 · methodology

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Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages

  1. [1]

    Knowledge-Centric Hallucination Detection

    URLhttps://aclanthology.org/2022.findings-aacl.36/. Dang Nguyen, Chacha Chen, He He, and Chenhao Tan. Pragmatic radiology report generation. In Machine Learning for Health (ML4H), pp. 385–402. PMLR, 2023. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gret...

    work page doi:10.18653/v1/2024 2022

  2. [2]

    You are givenN frames from a video shown in chronological order

    Association for Computational Linguistics. doi: 10.18653/v1/D18-1448. URL https: //aclanthology.org/D18-1448/. A APPENDIX B DATAPREPROCESSING ANDFILTERING FORMIMIC-CXR To construct a reliable text-only test set from MIMIC-CXR, we applied several filtering steps to the raw reports: Step 1: Identify single-report patients.We first traversed the report direc...

    work page doi:10.18653/v1/d18-1448

  3. [3]

    Generated Sentences:

    sentence 2 ... Generated Sentences:

    work page

  4. [6]

    [source sentence ids]

    work page

  5. [7]

    Example: Source Sentences:

    [source sentence ids] ... Example: Source Sentences:

    work page

  6. [8]

    The patient has a fever

    work page

  7. [9]

    Generated Sentences:

    The patient complains of headache. Generated Sentences:

    work page

  8. [10]

    The patient is experiencing fever and headache. Output:

    work page

  9. [11]

    For each generated summary sentence, identify the source elements (sentences and/or image) it can be attributed to

    [0, 1] Now attribute the following: Source Sentences: {source} Generated Sentences: {summary} Output: 16 Attribution Prompt You are given a list of source sentences (the text contains an “¡image¿” placeholder) and one associated image. For each generated summary sentence, identify the source elements (sentences and/or image) it can be attributed to. Input...

    work page

  10. [12]

    Generated Sentences:

    source sentence 2 ... Generated Sentences:

    work page

  11. [13]

    generated sentence 1

    work page

  12. [14]

    Output Format:

    generated sentence 2 ... Output Format:

    work page

  13. [15]

    [source ids and/or IMG]

    work page

  14. [16]

    Example: Image shows a red eye

    [source ids and/or IMG] ... Example: Image shows a red eye. Source Sentences:

    work page

  15. [17]

    Doctor: Do you have eye pain?

    work page

  16. [18]

    <image> Generated Sentences:

    Patient: Yes, my right eye is very red. <image> Generated Sentences:

    work page

  17. [19]

    The patient has eye redness. Output:

    work page

  18. [20]

    [1, IMG] Now attribute the following: Source Sentences: {source} Generated Sentences: {summary} Output: 17

    work page