Do Vision-Language Models See or Guess? Measuring and Reducing Textual-Prior Reliance with a Phrasing-Controlled Benchmark
arXiv:2606.10400v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly deployed where answers must follow from what is in the image, yet they often answer from textual priors, the question's phrasing together with memorized world knowledge, rather than from the image itself, which inflates benchmark scores and yields confident but ungrounded answers. Existing benchmarks rarely isolate this behavior, since each image is usually paired with a single fixed question. To measure the reliance, we build a 540-image benchmark across six reasoning categories and generate four qu
The proliferation of Vision-Language Models (VLMs) across various applications necessitates robust methods to evaluate their true comprehension versus superficial reliance on textual cues and memorized knowledge.
A strategic reader should care because this research directly addresses a critical weakness in current AI systems, highlighting the risk of deploying models that appear capable but are brittle and untrustworthy in real-world scenarios requiring true visual grounding.
This research introduces a novel benchmark that exposes VLM reliance on textual priors over visual evidence, providing a concrete tool to measure and mitigate this issue, thereby moving towards more reliable and interpretable AI.
- · AI safety researchers
- · Developers of robust VLM applications
- · Industries requiring high-integrity AI
- · Companies building explainable AI
- · Developers relying solely on current VLM benchmarks
- · Applications where VLM accuracy is critical but unverified
- · Companies with undisclosed VLM weaknesses
VLMs will be rigorously tested against benchmarks specifically designed to identify and penalize reliance on textual priors.
Model architectures and training methodologies will evolve to prioritize true visual understanding and reduce susceptibility to linguistic shortcuts.
Public and regulatory scrutiny of VLM deployment will intensify, demanding greater transparency around model limitations and grounding capabilities.
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