Researchers introduce PVFH-Bench to evaluate a specific hallucination mode in Large Vision-Language Models where models anchor to false textual premises. This failure occurs when models accept incorrect text inputs and generate responses about absent but visually plausible objects. The benchmark aims to quantify this perceptual bias, offering a clearer metric for model reliability in visual question answering and embodied agent tasks.
- PVFH highlights a new failure mode where text premises override visual evidence in VLMs.
- Models may hallucinate visually plausible objects that are absent but anchored by false text.
- PVFH-Bench provides a systematic way to measure this specific perceptual bias.
- Reliability in visual QA and human-AI decision support depends on mitigating these errors.