{
 "id": "atamiles/VLURes",
 "owner": "atamiles",
 "name": "VLURes",
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 "last_modified": "2026-07-17T02:54:55.000Z",
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 "citation": "@inproceedings{atuhurra-etal-2026-vlures,\n    title = \"{VLUR}es: Benchmarking Long-Text Grounding and Cross-Lingual Robustness in Vision Language Models\",\n    author = \"Atuhurra, Jesse  and\n      Ali, Iqra  and\n      Iwakura, Tomoya  and\n      Kamigaito, Hidetaka  and\n      Hiraoka, Tatsuya\",\n    editor = \"Liakata, Maria  and\n      Moreira, Viviane P.  and\n      Zhang, Jiajun  and\n      Jurgens, David\",\n    booktitle = \"Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026\",\n    month = jul,\n    year = \"2026\",\n    address = \"San Diego, California, United States\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2026.findings-acl.1367/\",\n    pages = \"27426--27481\",\n    ISBN = \"979-8-89176-395-1\",\n    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** pr",
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 "revision": "54b0054247890e943fe43c876cdcf874d2af41db",
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