← Search

Table · dataset · 2025

KRETA

Listed in Hugging Face Datasets

KRETA Benchmark 🤗 KRETA | 📖 Paper | 🏆 Leaderboard | 💻 Github KRETA: A Benchmark for Korean Reading and Reasoning in Text-Rich VQA Attuned to Diverse Visual Contexts (EMNLP 2025 Main Conference) Taebaek Hwang*, Minseo Kim*, Gisang Lee, Seonuk Kim, Hyunjun Eun

Description

Abstract

Understanding and reasoning over text within visual contexts poses a significant challenge for Vision-Language Models (VLMs), given the complexity and diversity of real-world scenarios. To address this… See the full description on the dataset page: huggingface.co/datasets/tabtoyou/KRETA.

Links

Where it is published

Documentation and papers

Catalogue records · 1

Topics

Stated by source
text · visual question answering
Inferred from text
Artificial intelligence 71% · Text 75%
Provenance · 1 source records, 11 field assertions
SourceKeyLast seenRaw
Hugging Face Datasetstabtoyou/KRETA14 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · Hugging Faceconnector:huggingface@1.0.0/gated
concepts[field].anzsrc:group:4602enrichment · Hugging Facetaxonomy-embedding@1.1.0title+keywords+description (71%)
concepts[field].local:field:computer-science-aimapping · Hugging Faceconnector:huggingface@1.0.0
concepts[modality].hf_modality:textsource · Hugging Faceconnector:huggingface@1.0.0
concepts[modality].local:modality:textenrichment · Hugging Facekeyword-concept-rules@1.0.0title+description (75%)
concepts[task].hf_task:visual-question-answeringsource · Hugging Faceconnector:huggingface@1.0.0/tags[task_categories:*]
created_datesource · Hugging Faceconnector:huggingface@1.0.0
descriptionsource · Hugging Faceconnector:huggingface@1.0.0/description
publication_datesource · Hugging Faceconnector:huggingface@1.0.0
titlesource · Hugging Faceconnector:huggingface@1.0.0/id
updated_datesource · Hugging Faceconnector:huggingface@1.0.0