{
 "id": "katebor/SciVQA",
 "owner": "katebor",
 "name": "SciVQA",
 "downloads": 517,
 "likes": 3,
 "last_modified": "2025-08-03T16:03:05.000Z",
 "stated_licence": "mit",
 "stated_from": "tag",
 "licence_name": null,
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 "gated": false,
 "tasks": [
  "visual-question-answering"
 ],
 "citation": "@inproceedings{borisova-etal-2025-scivqa,\n    title = \"{S}ci{VQA} 2025: Overview of the First Scientific Visual Question Answering Shared Task\",\n    author = \"Borisova, Ekaterina  and\n      Rauscher, Nikolas  and\n      Rehm, Georg\",\n    editor = \"Ghosal, Tirthankar  and\n      Mayr, Philipp  and\n      Singh, Amanpreet  and\n      Naik, Aakanksha  and\n      Rehm, Georg  and\n      Freitag, Dayne  and\n      Li, Dan  and\n      Schimmler, Sonja  and\n      De Waard, Anita\",\n    booktitle = \"Proceedings of the Fifth Workshop on Scholarly Document Processing (SDP 2025)\",\n    month = jul,\n    year = \"2025\",\n    address = \"Vienna, Austria\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.sdp-1.18/\",\n    pages = \"182--210\",\n    ISBN = \"979-8-89176-265-7\",\n    abstract = \"This paper provides an overview of the First Scientific Visual Question Answering (SciVQA) shared task conducted as part of the Fifth Scholarly Document Processing workshop (SDP 2025). SciVQA aims to explore the capabilities of current multimodal large language models (MLLMs) in reasoning over figures from scholarly publications for question answering (QA). The main focus of the challenge is on closed-ended visual and non-visual QA pairs. We developed the novel SciVQA benchmark comprising 3,000 images of figures and a total of 21,000 QA pairs. The shared task received seven submissions, with the best performing system achieving an average F1 score of approx. 0.86 acros",
 "signal": "permissive",
 "reason": "The declared licence (mit) is classified as permissive.",
 "revision": "7aa5cf20bf60e6c244b017d7e14d7b8f9100cda7",
 "stated_evidence": "https://huggingface.co/datasets/katebor/SciVQA/blob/7aa5cf20bf60e6c244b017d7e14d7b8f9100cda7/README.md",
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}