When to Trust the Answer: Question-Aligned Semantic Nearest Neighbor Entropy for Safer Surgical VQA

• URL: http://arxiv.org/abs/2511.01458v1
• Date: Mon, 03 Nov 2025 11:18:21 GMT
• Title: When to Trust the Answer: Question-Aligned Semantic Nearest Neighbor Entropy for Safer Surgical VQA
• Abstract summary: Safety and reliability are essential for deploying Visual Question Answering (VQA) in surgery.<n>We study uncertainty estimation as a key enabler of safer decision making.<n>We introduce Question Aligned Semantic Nearest Neighbor Entropy (QA-SNNE), a black box uncertainty estimator.
• Score: 11.804829233549704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safety and reliability are essential for deploying Visual Question Answering (VQA) in surgery, where incorrect or ambiguous responses can harm the patient. Most surgical VQA research focuses on accuracy or linguistic quality while overlooking safety behaviors such as ambiguity awareness, referral to human experts, or triggering a second opinion. Inspired by Automatic Failure Detection (AFD), we study uncertainty estimation as a key enabler of safer decision making. We introduce Question Aligned Semantic Nearest Neighbor Entropy (QA-SNNE), a black box uncertainty estimator that incorporates question semantics into prediction confidence. It measures semantic entropy by comparing generated answers with nearest neighbors in a medical text embedding space, conditioned on the question. We evaluate five models, including domain specific Parameter-Efficient Fine-Tuned (PEFT) models and zero-shot Large Vision-Language Models (LVLMs), on EndoVis18-VQA and PitVQA. PEFT models degrade under mild paraphrasing, while LVLMs are more resilient. Across three LVLMs and two PEFT baselines, QA-SNNE improves AUROC in most in-template settings and enhances hallucination detection. The Area Under the ROC Curve (AUROC) increases by 15-38% for zero-shot models, with gains maintained under out-of-template stress. QA-SNNE offers a practical and interpretable step toward AFD in surgical VQA by linking semantic uncertainty to question context. Combining LVLM backbones with question aligned uncertainty estimation can improve safety and clinician trust. The code and model are available at https://github.com/DennisPierantozzi/QASNNE

Related papers

• Auditing Frontier Vision-Language Models for Trustworthy Medical VQA: Grounding Failures, Format Collapse, and Domain Adaptation [4.177028541321909]
  We audit five vision-language models (VLMs) on Medical VQA along two trust-relevant axes.<n>Models localize anatomical and pathological targets poorly.<n>A self-grounding pipeline, where the same model localizes then answers, degrades VQA accuracy.
  arXiv  Detail & Related papers  (2026-04-30T11:11:47Z)
• Learning Uncertainty from Sequential Internal Dispersion in Large Language Models [52.29267172760918]
  Uncertainty estimation is a promising approach to detect hallucinations in large language models.<n>Recent approaches commonly depend on model internal states to estimate uncertainty.<n>We present Sequential Internal Variance Representation (SIVR), a supervised hallucination detection framework.
  arXiv  Detail & Related papers  (2026-04-17T06:31:29Z)
• Decomposed Prompting Does Not Fix Knowledge Gaps, But Helps Models Say "I Don't Know" [47.930782177987446]
  Large language models often struggle to recognize their knowledge limits in closed-book question answering, leading to confident hallucinations.<n>We evaluate three task-equivalent prompting regimes: Direct, Assistive, and Incremental, across different model scales and multi-hop QA benchmarks.<n>Because factual knowledge is stable while hallucinations are agreement, cross-regime provides a precise signal of internal uncertainty.
  arXiv  Detail & Related papers  (2026-02-04T18:39:58Z)
• Knowing When to Answer: Adaptive Confidence Refinement for Reliable Audio-Visual Question Answering [15.39457034915546]
  We present a formal problem formulation for textitReliable Audio-Visual Question Answering ($mathcalR$-AVQA), where we prefer abstention over answering incorrectly.<n>We propose Adaptive Confidence Refinement (ACR), a lightweight method to further enhance the performance of $mathcalR$-AVQA.
  arXiv  Detail & Related papers  (2026-02-04T08:35:33Z)
• Mind the Ambiguity: Aleatoric Uncertainty Quantification in LLMs for Safe Medical Question Answering [6.782185804809171]
  Large Language Models in Medical Question Answering severely hampered by ambiguous user queries.<n>In this paper, we formalize this challenge by linking input ambiguity to aleatoric uncertainty (AU), which is the irreducible uncertainty arising from underspecified input.<n>We introduce a novel AU-guided "Clarify-Before-Answer" framework, which incorporates AU-Probe - a lightweight module that detects input ambiguity directly from hidden states.
  arXiv  Detail & Related papers  (2026-01-24T03:44:08Z)
• Decoupling Clinical and Class-Agnostic Features for Reliable Few-Shot Adaptation under Shift [12.373281238541296]
  Medical vision-language models (VLMs) offer promise for clinical decision support, yet their reliability under distribution shifts remains a major concern for safe deployment.<n>We propose DRiFt, a structured feature decoupling framework that explicitly separates clinically relevant signals from task-agnostic noise.<n>Our approach improves in-distribution performance by +11.4% Top-1 accuracy and +3.3% Macro-F1 over prior prompt-based methods.
  arXiv  Detail & Related papers  (2025-09-11T12:26:57Z)
• SURE-VQA: Systematic Understanding of Robustness Evaluation in Medical VQA Tasks [2.033441577169909]
  Vision-Language Models (VLMs) have great potential in medical tasks, like Visual Question Answering (VQA)<n>Their robustness to distribution shifts on unseen data remains a key concern for safe deployment.<n>We introduce a novel framework, called SURE-VQA, centered around three key requirements to overcome current pitfalls.
  arXiv  Detail & Related papers  (2024-11-29T13:22:52Z)
• Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering [51.26412822853409]
  We present a novel personalized federated learning (pFL) method for medical visual question answering (VQA) models. Our method introduces learnable prompts into a Transformer architecture to efficiently train it on diverse medical datasets without massive computational costs.
  arXiv  Detail & Related papers  (2024-10-23T00:31:17Z)
• Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond [52.246494389096654]
  This paper introduces Word-Sequence Entropy (WSE), a method that calibrates uncertainty at both the word and sequence levels. We compare WSE with six baseline methods on five free-form medical QA datasets, utilizing seven popular large language models (LLMs)
  arXiv  Detail & Related papers  (2024-02-22T03:46:08Z)
• Uncertainty-aware Language Modeling for Selective Question Answering [107.47864420630923]
  We present an automatic large language model (LLM) conversion approach that produces uncertainty-aware LLMs. Our approach is model- and data-agnostic, is computationally-efficient, and does not rely on external models or systems.
  arXiv  Detail & Related papers  (2023-11-26T22:47:54Z)
• Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly [100.60560477391732]
  We promote a problem formulation for reliable visual question answering (VQA) We analyze both their coverage, the portion of questions answered, and risk, the error on that portion. We find that although the best performing models achieve over 71% accuracy on the VQA v2 dataset, introducing the option to abstain limits them to answering less than 8% of the questions to achieve a low risk of error (i.e., 1%) This motivates us to utilize a multimodal selection function to directly estimate the correctness of the predicted answers, which we show can triple the coverage from, for example, 5.0% to 16.7% at
  arXiv  Detail & Related papers  (2022-04-28T16:51:27Z)
• Counterfactual Variable Control for Robust and Interpretable Question Answering [57.25261576239862]
  Deep neural network based question answering (QA) models are neither robust nor explainable in many cases. In this paper, we inspect such spurious "capability" of QA models using causal inference. We propose a novel approach called Counterfactual Variable Control (CVC) that explicitly mitigates any shortcut correlation.
  arXiv  Detail & Related papers  (2020-10-12T10:09:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.