PSF-Med: Measuring and Explaining Paraphrase Sensitivity in Medical Vision Language Models

TL;DR

This paper introduces PSF-Med, a comprehensive benchmark for measuring paraphrase sensitivity in medical vision language models, revealing their reliance on language priors and identifying mechanisms behind answer flips.

Contribution

The study presents PSF-Med, a large-scale benchmark with validated paraphrases, and analyzes model mechanisms, showing how specific features influence answer stability and proposing methods to improve robustness.

Findings

Flip rates range from 3% to 37% across models.

Removing a sparse feature reduces flip rates by 31%.

Models often rely on language priors even without visual input.

Abstract

Medical Vision Language Models (VLMs) can change their answers when clinicians rephrase the same question, a failure mode that threatens deployment safety. We introduce PSF-Med, a benchmark of 26,850 chest X-ray questions paired with 92,856 meaning-preserving paraphrases across MIMIC-CXR, PadChest, and VinDr-CXR, spanning clinical populations in the US, Spain, and Vietnam. Every paraphrase is validated by an LLM judge using a bidirectional clinical entailment rubric, with 91.6% cross-family agreement. Across nine VLMs, including general-purpose models, we find flip rates from 3% to 37%. However, low flip rate does not imply visual grounding: text-only baselines show that some models stay consistent even when the image is removed, suggesting they rely on language priors. To study mechanisms in one model, we apply GemmaScope 2 Sparse Autoencoders (SAEs) to MedGemma 4B and analyze FlipBank, a curated set of 158 flip cases. We identify a sparse feature at layer 17 that correlates with prompt framing and predicts decision margin shifts. In causal patching, removing this feature's contribution recovers 45% of the yes-minus-no logit margin on average and fully reverses 15% of flips. Acting on this finding, we show that clamping the identified feature at inference reduces flip rates by 31% relative with only a 1.3 percentage-point accuracy cost, while also decreasing text-prior reliance. These results suggest that flip rate alone is not enough; robustness evaluations should test both paraphrase stability and image reliance.