Evaluating LLM Robustness Under Domain-Specific Prompt Perturbations in Public Health Applications

• URL: http://arxiv.org/abs/2607.06913v1
• Date: Wed, 08 Jul 2026 02:04:47 GMT
• Title: Evaluating LLM Robustness Under Domain-Specific Prompt Perturbations in Public Health Applications
• Abstract summary: Large language models (LLMs) are increasingly applied in public health applications, yet their robustness to non-clinical user inputs remains underexplored.<n>We propose a domain specific robustness benchmark that evaluates LLMs under two perturbations: misinformation framing (MF) and layperson rewriting (LR)<n>Experiments show MF degrades accuracy by 7.2 pp on average with prediction flip rates of 9-38 percent, even when claims are explicitly labelled as unsupported.
• Score: 3.32715997905088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) are increasingly applied in public health applications, yet their robustness to non-clinical user inputs remains underexplored. We propose a domain specific robustness benchmark that evaluates LLMs under two perturbation types that commonly arise when non-clinical users interact with health AI systems: misinformation framing (MF), where prompt might be injected by false health claims, and layperson rewriting (LR), where patients describe symptoms in everyday language rather than medical terminology. Our goal is to evaluate the stability of LLMs under these perturbation. Experiments show that MF degrades accuracy by 7.2 pp on average with prediction flip rates of 9-38 percent, even when claims are explicitly labelled as unsupported; LR causes only 1.4 pp degradation. These findings highlight two distinct deployment risks in public health settings: models may produce incorrect outputs when users unintentionally carry misinformation into their queries, and may misinterpret clinically relevant details when patients use informal language. Both risks call for perturbation-aware robustness evaluation beyond clean baseline benchmark

Related papers

• Beyond Scalar Scores: Exploring LLM-based Metrics for Clinical Significance Evaluation in Radiology Reports [49.5225801722164]
  Existing metrics obscure this requirement by reducing report quality to a medically ungrounded scalar.<n>We study this boundary using ReEvalMed benchmark as testbed and evaluate metric-level clinical significance.
  arXiv  Detail & Related papers  (2026-06-17T08:10:30Z)
• Testing the Black Box: Structural Barriers to Independent Evaluation of Consumer-Facing Health LLMs [2.526741713337939]
  We evaluate response variation and sycophancy in consumer-facing health LLMs under conditions resembling ordinary patient use.<n>We adapted validated instruments, including the Vaccination Attitudes Examination scale and reproductive attitudes scales, into multi-turn prompts.<n>Results: Factual prompts produced stable responses that masked sycophancy emerging over multi-turn conversation.
  arXiv  Detail & Related papers  (2026-06-07T07:01:15Z)
• When Large Language Models Fail in Healthcare: Evaluating Sensitivity to Prompt Variations [1.0152838128195467]
  Large Language Models (LLMs) are increasingly used in healthcare for tasks such as clinical question answering, diagnosis support, and report summarization.<n>Despite their promise, these models remain highly sensitive to subtle prompt perturbations, both lexical and syntactic, posing serious risks in safety-critical clinical applications.<n>We categorize perturbations into natural and adversarial types and examine their effect on model consistency, accuracy, and reliability in clinical reasoning tasks.
  arXiv  Detail & Related papers  (2026-06-05T13:07:11Z)
• Artificial Intolerance: Stigmatizing Language in Clinical Documentation Skews Large Language Model Decision-Making [13.79732306725696]
  Large Language Models (LLMs) are increasingly deployed in high-stakes domains such as clinical decision support and medical documentation.<n>We investigate whether frontier LLMs inherit and propagate this human bias when processing clinical text.
  arXiv  Detail & Related papers  (2026-05-17T02:28:25Z)
• CLEAR: Revealing How Noise and Ambiguity Degrade Reliability in LLMs for Medicine [9.346299965194746]
  We introduce the CLinical Evaluation of Ambiguity and Reliability (CLEAR) framework.<n>CLEAR perturbs the number of plausible answer options, the presence of a ground truth or abstention option, and the semantic framing of answer options.<n>Applying CLEAR on three benchmarks evaluated across 17 medical large language models reveals three notable limitations.
  arXiv  Detail & Related papers  (2026-05-01T18:23:24Z)
• Same Verdict, Different Reasons: LLM-as-a-Judge and Clinician Disagreement on Medical Chatbot Completeness [49.2667937337333]
  We stress-test this assumption for detecting incomplete patient-facing medical responses.<n>We evaluate three granularities (General-Likert, Analytical-Rubric, Dynamic-Checklist) and three backbone models across two clinician-annotated datasets.
  arXiv  Detail & Related papers  (2026-03-26T19:01:55Z)
• Robust or Suggestible? Exploring Non-Clinical Induction in LLM Drug-Safety Decisions [5.347177139463896]
  Large language models (LLMs) are increasingly applied in biomedical domains, yet their reliability in drug-safety prediction remains underexplored.<n>We assess two state-of-the-art models, ChatGPT-4o and Bio-Medical-Llama-3.8B, across diverse personas defined by education, marital status, employment, insurance, language, housing stability, and religion.
  arXiv  Detail & Related papers  (2025-10-15T14:11:11Z)
• EchoBench: Benchmarking Sycophancy in Medical Large Vision-Language Models [82.43729208063468]
  Recent benchmarks for medical Large Vision-Language Models (LVLMs) emphasize leaderboard accuracy, overlooking reliability and safety.<n>We study sycophancy -- models' tendency to uncritically echo user-provided information.<n>We introduce EchoBench, a benchmark to systematically evaluate sycophancy in medical LVLMs.
  arXiv  Detail & Related papers  (2025-09-24T14:09:55Z)
• Beyond Benchmarks: Dynamic, Automatic And Systematic Red-Teaming Agents For Trustworthy Medical Language Models [87.66870367661342]
  Large language models (LLMs) are used in AI applications in healthcare.<n>Red-teaming framework that continuously stress-test LLMs can reveal significant weaknesses in four safety-critical domains.<n>A suite of adversarial agents is applied to autonomously mutate test cases, identify/evolve unsafe-triggering strategies, and evaluate responses.<n>Our framework delivers an evolvable, scalable, and reliable safeguard for the next generation of medical AI.
  arXiv  Detail & Related papers  (2025-07-30T08:44:22Z)
• Fact or Guesswork? Evaluating Large Language Models' Medical Knowledge with Structured One-Hop Judgments [108.55277188617035]
  Large language models (LLMs) have been widely adopted in various downstream task domains, but their abilities to directly recall and apply factual medical knowledge remains under-explored.<n>We introduce the Medical Knowledge Judgment dataset (MKJ), a dataset derived from the Unified Medical Language System (UMLS), a comprehensive repository of standardized vocabularies and knowledge graphs.<n>Through a binary classification framework, MKJ evaluates LLMs' grasp of fundamental medical facts by having them assess the validity of concise, one-hop statements.
  arXiv  Detail & Related papers  (2025-02-20T05:27:51Z)
• "Knowing When You Don't Know": A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation [90.09260023184932]
  Retrieval-Augmented Generation (RAG) grounds Large Language Model (LLM) output by leveraging external knowledge sources to reduce factual hallucinations. NoMIRACL is a human-annotated dataset for evaluating LLM robustness in RAG across 18 typologically diverse languages. We measure relevance assessment using: (i) hallucination rate, measuring model tendency to hallucinate, when the answer is not present in passages in the non-relevant subset, and (ii) error rate, measuring model inaccuracy to recognize relevant passages in the relevant subset.
  arXiv  Detail & Related papers  (2023-12-18T17:18:04Z)

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.