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VLURes: Benchmarking Long-Text Grounding and Cross-Lingual Robustness in Vision Language Models

Domaine:

natural language processing

Type de record:

datasetpaper
Créateur:
AssAliAtuHir
Éditeur:
Und
Hôte:avatar
We introduce VLURes, a multilingual benchmark for evaluating Vision-Language Models (VLMs) under long-text grounding: selecting and reasoning over the image-relevant subset of article-length text that contains distractors and ungrounded claims. VLURes contains 4,000 web-curated image + long-text pairs across English (En), Japanese (Ja), Swahili (Sw), and Urdu (Ur) and 10 topical categories, and defines eight tasks spanning image-only perception (OR, SU, RU, SS, IC) and image+text grounding (ITM, Unrelatedness, VQA). To construct web-realistic pairs, we apply language-adapted CLIP alignment to select representative images and filter weakly grounded pages. Across 10 proprietary and open VLMs evaluated under zero-shot and one-shot prompting, with and without rationales, the best model (GPT-4o) reaches 90.8% overall accuracy but remains 6.7 points below human performance (97.5%) on Object Recognition, and cross-lingual sensitivity persists, while open models are substantially weaker and often lack reliable multilingual VL support. VLURes provides a practical testbed for long-text grounding and multilingual robustness in web-realistic agent settings.

Visit

doi.org

Tasks

computer visionimage-text retrieval

Languages

Swahili

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