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InstructBLIP models struggle to reliably determine answerability from visual evidence

By PulseAugur Editorial · [1 sources] ·

A new research paper analyzes the answerability signals in frozen InstructBLIP models, a type of vision-language model. The study found that common signals like predictive uncertainty, cross-modal alignment, and visual grounding are not reliable indicators of whether a model can actually answer a visual question based on the provided image. Even when specifically testing for visual dependence, the models showed weak discrimination between answerable and unanswerable questions. AI

IMPACT Highlights limitations in current vision-language models' ability to discern answerability, suggesting a need for improved methods beyond simple visual dependence.

RANK_REASON Academic paper analyzing model capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

InstructBLIP models struggle to reliably determine answerability from visual evidence

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Academic paper analyzing model capabilities. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Minjun Kim, Hyunmin Song, Changwon Lim ·

    Visual Dependence Does Not Imply Answerability: A Systematic Analysis of Answerability Signals in Frozen InstructBLIP

    arXiv:2610.17902v1 Announce Type: cross Abstract: Visual question answering models often produce plausible responses even when the available visual evidence is insufficient to support a valid answer. Although prior work has introduced benchmarks and abstention mechanisms for unan…

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