Query-Dependent Use of Generated Descriptions for Reliable Visual Question Answering
Vision-Language Models (VLMs) hallucinate objects that are not present, and a growing line of work tries to curb this by feeding the model its own generated caption as auxiliary evidence -- assuming that a caption, once available, is something to consume. We show this fails: naively appending a caption can lower accuracy rather than raise it, dropping Qwen2.5-VL-3B† on HallusionBench by nearly ten points. To understand why, we build GD-Probe, a diagnostic set that pairs a global and a detail question on the same image, so that any difference in caption effect is attributable to the question alone. Caption utility proves to be a per-query property: the same caption helps global questions a...
| Authors | Zeshang Li, Shuoyang Zhang |
|---|---|
| Primary category | cs.CV |
| Published | 3 May 2026 |
| Last updated at source | 28 September 2026 |
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