@inproceedings{atuhurra-etal-2026-vlures,
title = "{VLUR}es: Benchmarking Long-Text Grounding and Cross-Lingual Robustness in Vision Language Models",
author = "Atuhurra, Jesse and
Ali, Iqra and
Iwakura, Tomoya and
Kamigaito, Hidetaka and
Hiraoka, Tatsuya",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1367/",
doi = "10.18653/v1/2026.findings-acl.1367",
pages = "27426--27481",
ISBN = "979-8-89176-395-1",
abstract = "We introduce \textit{\textbf{VLURes}}, a multilingual benchmark for evaluating Vision-Language Models (VLMs) under \textit{long-text grounding}: selecting and reasoning over the image-relevant subset of article-length text that contains distractors and ungrounded claims. \textit{VLURes} contains \textbf{4,000} web-curated \textit{image + long-text} pairs across \textbf{English (En), Japanese (Ja), Swahili (Sw), and Urdu (Ur)} and \textbf{10} topical categories, and defines \textbf{eight} tasks spanning image-only perception (OR, SU, RU, SS, IC) and image+text grounding (ITM, \textit{Unrelatedness}, VQA). To construct web-realistic pairs, we apply language-adapted CLIP alignment to select representative images and filter weakly grounded pages. Across \textbf{10} proprietary and open VLMs evaluated under zero-shot and one-shot prompting, with and without rationales, the best model (GPT-4o) reaches \textbf{90.8{\%}} overall accuracy but remains \textbf{6.7} points below human performance (\textbf{97.5{\%}}) on Object Recognition, and cross-lingual sensitivity persists, while open models are substantially weaker and often lack reliable multilingual VL support. \textit{VLURes} provides a practical testbed for long-text grounding and multilingual robustness in web-realistic agent settings."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T VLURes: Benchmarking Long-Text Grounding and Cross-Lingual Robustness in Vision Language Models
%A Atuhurra, Jesse
%A Ali, Iqra
%A Iwakura, Tomoya
%A Kamigaito, Hidetaka
%A Hiraoka, Tatsuya
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F atuhurra-etal-2026-vlures
%X 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.
%R 10.18653/v1/2026.findings-acl.1367
%U https://aclanthology.org/2026.findings-acl.1367/
%U https://doi.org/10.18653/v1/2026.findings-acl.1367
%P 27426-27481
Markdown (Informal)
[VLURes: Benchmarking Long-Text Grounding and Cross-Lingual Robustness in Vision Language Models](https://aclanthology.org/2026.findings-acl.1367/) (Atuhurra et al., Findings 2026)
ACL