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Small Vision-Language Models Show Confidence Gap Under Image Degradation

By PulseAugur Editorial · [1 sources] ·

A recent study examined the confidence signals of two small vision-language models, Qwen2-VL-2B-Instruct and SmolVLM-Instruct, under realistic image degradation. The research found a significant discrepancy between the models' stated confidence in natural language and their internal token probabilities. While internal probabilities proved to be a more reliable indicator of errors, both models struggled to accurately assess their confidence when faced with severe underexposure, leading to a collapse in accuracy with minimal change in confidence signals. AI

IMPACT Highlights limitations in current small vision-language models regarding reliable uncertainty estimation, crucial for safe deployment.

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

Read on arXiv cs.CL →

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

Small Vision-Language Models Show Confidence Gap Under Image Degradation

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

  1. arXiv cs.CL TIER_1 English(EN) · M M Asif Ferdous ·

    Small Vision-Language Models Know When They Are Wrong But Cannot Say So: A Two-Model Study of Stated versus Internal Confidence Under Realistic Image Degradation

    arXiv:2607.22034v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed on consumer hardware where input images are degraded by compression, camera shake, and poor lighting. In such settings, a reliable uncertainty signal matters more than raw ac…

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