--- dataset_info: features: - name: query_id dtype: int64 - name: source_type dtype: string - name: query_type dtype: string - name: query_format dtype: string - name: query dtype: string - name: doc_id dtype: string - name: image_id dtype: int64 - name: image dtype: image - name: markdown dtype: string - name: elements dtype: string - name: page_number_in_doc dtype: int64 splits: - name: train num_bytes: 270527680486.72 num_examples: 310226 download_size: 251526672926 dataset_size: 270527680486.72 configs: - config_name: default data_files: - split: train path: data/train-* task_categories: - document-question-answering - visual-document-retrieval language: - ko tags: - Visual Retrieving - Industrial RAG - datadesigner size_categories: - 100K ๐Ÿ”Ž Ko-VDR Train Public v2

Korean VDR Train

> [!NOTE] > **Changes from v1** > - Collected more diverse documents to increase the number of queries. > - Partially revised prompts to improve generation quality. > - Applied relevance mapping in both the generation and filtering stages, retaining only queries where relevance mapping was consistently performed in both stages. > - Applied rule-based filtering to remove low-quality queries. > [!NOTE] > For a high-level overview of how the dataset was generated, see [PIPELINE.md](https://huggingface.co/datasets/NomaDamas/ko-vdr-train-public-v2.0/blob/main/PIPELINE.md). This dataset is a training dataset for Korean Visual Document Retrieval. It includes 310,226 query-page pairs (146,752 unique queries) generated from 49 Korean government and public institution PDF documents using LLM-based (Solar Pro 3) synthetic query generation. Queries are generated from two sources: page-level summaries (87%) and direct page context (13%), covering 7 query types (compare-contrast, open-ended, enumerative, multi-hop, extractive, numerical, boolean) in instruction, question, and keyword formats. ## Links * **Github:** [https://github.com/whybe-choi/kovidore-data-generator](https://github.com/whybe-choi/kovidore-data-generator) ### Dataset Summary - Description: Training data for Korean Visual Document Retrieval, generated from Korean government and public institution reports - Language: ko - Document Types: Government reports, guidelines, manuals, survey reports ### Dataset Statistics - Total Documents : 49 - Total Pages : 7,548 - Total Queries : 146,752 - Average number of pages per query : 2.0 ### Number of Relevant Pages per Query | # Relevant Pages | # Queries | |:-:|:-:| | 1 | 44,244 | | 2 | 69,339 | | 3 | 24,138 | | 4 | 6,319 | | 5 | 1,814 | | 6 | 552 | | 7 | 204 | | 8 | 101 | | 9 | 41 | ### Queries per Document | Doc ID | Context | Summary | Count | |--------|---------|---------|-------| | ๊ธฐํ›„์—๋„ˆ์ง€ํ™˜๊ฒฝ๋ถ€_์—๋„ˆ์ง€์ด์กฐ์‚ฌ_20241130 | 2,987 | 2,630 | 5,617 | | 25๋…„_์ฃผ์š”์—…๋ฌด๊ณ„ํš(๊ฒŒ์‹œ์šฉ) | 2,603 | 2,665 | 5,268 | | 2025๋…„_์ง€๋ฐฉ๊ณต๋ฌด์›_์ธ์‚ฌ์‹ค๋ฌด | 2,048 | 2,720 | 4,768 | | (์ตœ์ข…๋ณด๊ณ ์„œ)_๊ตญ์ œ_OTT_์‚ฐ์—…_์‹คํƒœ์กฐ์‚ฌ_๋ฐ_๊ตญ๋‚ด_OTT_๊ธ€๋กœ๋ฒŒ_์ง„์ถœ_๋ฐฉ์•ˆ_์—ฐ๊ตฌ | 1,694 | 2,706 | 4,400 | | 2023-2024_ํ•ญ๋งŒ์—…๋ฌดํŽธ๋žŒ | 631 | 3,177 | 3,808 | | ๋‚ด์ง€-KOSI_์ค‘์†Œ๊ธฐ์—…๋™ํ–ฅ_2026๋…„_2์›”ํ˜ธ | 96 | 3,382 | 3,478 | | ํ•ด์–‘์ˆ˜์‚ฐ๋ถ€_๋ฌด์ธ๋„์„œ_100์„ _20221231 | 183 | 3,273 | 3,456 | | ๊ตญํ† ๊ตํ†ต๋ถ€_ํ•ด์™ธ๊ฑด์„ค_์„ธ๋ฌด์—…๋ฌด_๋งค๋‰ด์–ผ_20220404 | 750 | 2,657 | 3,407 | | 2019๋…„_์ œ6์ฐจ_์ž‘์—…ํ™˜๊ฒฝ์‹คํƒœ์กฐ์‚ฌ_์ตœ์ข…๋ณด๊ณ ์„œ(์ง€์‚ฌ_์ œ์™ธ) | 374 | 2,896 | 3,270 | | 2024_ํšŒ๊ณ„์—ฐ๋„_๊ธฐ์—…์ฒด๋…ธ๋™๋น„์šฉ์กฐ์‚ฌ_๋ณด๊ณ ์„œ | 506 | 2,727 | 3,233 | | ๊ตญํ† ๊ตํ†ต๋ถ€_ํ•ด์™ธ๊ฑด์„ค_๋ฒ•๋ฅ ์ปจ์„คํŒ…_์‚ฌ๋ก€_20240628 | 486 | 2,729 | 3,215 | | 2022๋…„๋„๊ตญ๊ฐ€์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—…์ƒ์œ„ํ‰๊ฐ€๋ณด๊ณ ์„œ(์ค‘๊ฐ„ํ‰๊ฐ€)_์ตœ์ข… | 503 | 2,691 | 3,194 | | ๊ฒฝ์ƒ๋ถ๋„_(PDF)์ธ์‚ผ์žฌ๋ฐฐ์ „์„œ_20210825 | 147 | 3,025 | 3,172 | | 2025_์‚ฐ์—…๋ณด๊ณ ์„œ(๋ฐฉ์œ„์‚ฐ์—…)_๋ผํ‹ด์•„๋ฉ”๋ฆฌ์นด_ํ˜‘๋ ฅ์„ผํ„ฐ | 41 | 3,119 | 3,160 | | 1.์กฐ์‚ฌ์š”์•ฝ(2024๋ถ€์‚ฐ๋ฐฉ๋ฌธ๊ด€๊ด‘๊ฐ์‹คํƒœ์กฐ์‚ฌ) | 222 | 2,912 | 3,134 | | ๊ตญํ† ์•ˆ์ „๊ด€๋ฆฌ์›_์Šค๋งˆํŠธ_์•ˆ์ „์œ ์ง€๊ด€๋ฆฌ_์‹œ์„ค๋ฌผ_ํ™•๋Œ€๋ฐฉ์•ˆ_๋งˆ๋ จ_์šฉ์—ญ_๋ณด๊ณ ์„œ_2024 | 680 | 2,414 | 3,094 | | 2026๋…„_๊ณต๋ฌด์›_์ธ์žฌ๊ฐœ๋ฐœ_์ข…ํ•ฉ๊ณ„ํš | 285 | 2,774 | 3,059 | | ์ƒ์ฒด์ •๋ณด_๋ณดํ˜ธ_์•ˆ๋‚ด์„œ(2024.12) | 285 | 2,740 | 3,025 | | 2025๋…„_๊ต์œก์šด์˜๊ณ„ํš | 103 | 2,919 | 3,022 | | ๊ทธ๋žœ๋“œ์ฝ”๋ฆฌ์•„๋ ˆ์ €(์ฃผ)_์นด์ง€๋…ธ_๋น„์ฆˆ๋‹ˆ์Šค์™€_์ œ๋„_20140219 | 64 | 2,923 | 2,987 | | ์ œ3์ฐจ_ํ•ด์–‘์ˆ˜์‚ฐ๋ฐœ์ „๊ธฐ๋ณธ๊ณ„ํš(2021-2030) | 701 | 2,272 | 2,973 | | 2025_์‚ฐ์—…๋ณด๊ณ ์„œ(์ œ์•ฝ๋ฐ”์ด์˜ค)_๋ผํ‹ด์•„๋ฉ”๋ฆฌ์นด_ํ˜‘๋ ฅ์„ผํ„ฐ | 44 | 2,893 | 2,937 | | ํ•œ๊ตญ์ธํ„ฐ๋„ท์ง„ํฅ์›_๊ฐœ์ธ์ •๋ณด_์œ ์ถœ_์‹ ๊ณ _๋™ํ–ฅ_๋ฐ_์˜ˆ๋ฐฉ_๋ฐฉ๋ฒ•_20241231 | 132 | 2,754 | 2,886 | | (์ตœ์ข…)UN๊ฐœํ™ฉ(2019)-๋‚ด์ง€-์ตœ์ข…(์›น์šฉ) | 650 | 2,220 | 2,870 | | ํ•œ๊ตญ์›์ž๋ ฅํ™˜๊ฒฝ๊ณต๋‹จ_์ฒ˜๋ถ„์‹œ์„ค_๋ถ€์ง€์ฃผ๋ณ€_๋ฐฉ์‚ฌ์„ ํ™˜๊ฒฝ์กฐ์‚ฌ_๋ณด๊ณ ์„œ_20250831 | 255 | 2,585 | 2,840 | | (์ตœ์ข…๋ณด๊ณ ์„œ)_๋””์ง€ํ„ธ๋ฏธ๋””์–ด_ํ—ˆ๋ธŒ_์กฐ์„ฑ์„_์œ„ํ•œ_๋น›๋งˆ๋ฃจ_์ค‘์žฅ๊ธฐ_์ „๋žต_์—ฐ๊ตฌ | 170 | 2,599 | 2,769 | | ํ•ฉ์„ฑ๋ฐ์ดํ„ฐ_์ƒ์„ฑํ™œ์šฉ_์•ˆ๋‚ด์„œ(2024.12) | 388 | 2,372 | 2,760 | | ๊ฐœ์ธ์ •๋ณด_์œ ์ถœ_๋“ฑ_์‚ฌ๊ณ _๋Œ€์‘_๋งค๋‰ด์–ผ(2024.3) | 177 | 2,572 | 2,749 | | 2024๋…„๋„ํ•˜๋ฐ˜๊ธฐ๊ตญ๊ฐ€์—ฐ๊ตฌ๊ฐœ๋ฐœ์‚ฌ์—…ํŠน์ •ํ‰๊ฐ€๋ณด๊ณ ์„œ(๋‹ค๋ถ€์ฒ˜๊ณต๋™์ถ”์ง„์‚ฌ์—…๊ตฐ) | 393 | 2,351 | 2,744 | | ์ œ1์ฐจ_๋Œ€ํ•œ๋ฏผ๊ตญ_๊ณต๊ณต์™ธ๊ต_๊ธฐ๋ณธ๊ณ„ํš(2017-2021)_(์ตœ์ข…๋ณธ) | 50 | 2,673 | 2,723 | | ํ•œ๊ตญ๋…ธ์ธ์ธ๋ ฅ๊ฐœ๋ฐœ์›_๋…ธ์ธ_์ผ์ž๋ฆฌ_๋ฐ_์‚ฌํšŒํ™œ๋™_์ง€์›์‚ฌ์—…_์‹œํ–‰_20๋…„์˜_์„ฑ๊ณผ | 30 | 2,682 | 2,712 | | ์ˆ˜๋„๊ถŒ๋งค๋ฆฝ์ง€๊ด€๋ฆฌ๊ณต์‚ฌ_๊ด€๋ฆฌํ˜•๋งค๋ฆฝ์ง€_์กฐ์‚ฌ๊ฒฐ๊ณผ๋ณด๊ณ ์„œ_20230102 | 245 | 2,467 | 2,712 | | ๊ณผํ•™๊ธฐ์ˆ ์ •๋ณดํ†ต์‹ ๋ถ€_๊ตญ๋ฆฝ์ „ํŒŒ์—ฐ๊ตฌ์›_ICT_์œต๋ณตํ•ฉ_์‹œ์„ค์˜_์•ˆ์ „ํ•œ_์ „์žํŒŒ_ํ™˜๊ฒฝ_๊ธฐ๋ฐ˜_์กฐ์„ฑ_์—ฐ๊ตฌ_20241231 | 221 | 2,461 | 2,682 | | (์ตœ์ข…๋ณด๊ณ ์„œ)_๊ตญ๋‚ด์™ธ_์˜จ๋ผ์ธ_๋™์˜์ƒ_๋ฏธ๋””์–ด์ฝ˜ํ…์ธ _์‹œ์žฅ_์ „๋ง_๋ฐ_์ •์ฑ…_์ถ”์ง„๋ฐฉํ–ฅ_์—ฐ๊ตฌ | 113 | 2,503 | 2,616 | | ์ œ3์ฐจ_ํ™˜๊ฒฝ๊ด€๋ฆฌํ•ด์—ญ_๊ธฐ๋ณธ๊ณ„ํš | 419 | 2,191 | 2,610 | | ๋ฐ์ดํ„ฐ์—_๋‹ด๊ธด_์„œ์šธ๊ตํ†ต_2023 | 47 | 2,562 | 2,609 | | ์ง€์ ๋ณ„_์ธ์ž…๊ฐ€๋Šฅ๋Ÿ‰_์ตœ์ข…_๋ถ„์„_๊ฒฐ๊ณผ | 8 | 2,591 | 2,599 | | ํ•œ๊ตญ์ˆ˜๋ ฅ์›์ž๋ ฅ(์ฃผ)_i_SMR_๋ฐ_SSNC_์„ค๋ช…์ž๋ฃŒ_20250829 | 111 | 2,413 | 2,524 | | ํ•œ๊ตญ์–ธ๋ก ์ง„ํฅ์žฌ๋‹จ_๋ฏธ๋””์–ด์ด์Šˆ_๊ด‘๊ณ ์š”๊ธˆ์ œ_๋„์ž…์„_์•ž๋‘”_๋„ทํ”Œ๋ฆญ์Šค์—_๋Œ€ํ•œ_์ธ์‹_๋ฐ_์ด์šฉ_์กฐ์‚ฌ_20220928 | 15 | 2,502 | 2,517 | | (์ตœ์ข…๋ณด๊ณ ์„œ)_๋””์ง€ํ„ธ๋ฏธ๋””์–ด_์‹ ์‚ฐ์—…_์ง„ํฅ_๋ฐฉ์•ˆ_๋ฐ_์ธ๋ ฅ์ˆ˜๊ธ‰_๊ธฐ์ดˆ์กฐ์‚ฌ์—_๊ด€ํ•œ_์—ฐ๊ตฌ | 134 | 2,364 | 2,498 | | ํ–‰์ •์•ˆ์ „๋ถ€_๋ชจ๋ฐ”์ผ_์ „์ž์ •๋ถ€์„œ๋น„์Šค_์•ฑ_์†Œ์Šค์ฝ”๋“œ_๊ฒ€์ฆ_๊ฐ€์ด๋“œ๋ผ์ธ_20211029 | 169 | 2,278 | 2,447 | | ์ œ2์ฐจ_ํ™˜๊ฒฝ๊ด€๋ฆฌํ•ด์—ญ_๊ธฐ๋ณธ๊ณ„ํš | 79 | 2,367 | 2,446 | | ํ•œ๊ตญ์–ธ๋ก ์ง„ํฅ์žฌ๋‹จ_๋ฏธ๋””์–ด์ด์Šˆ_์ด๋Œ€๋‚จ_ํ˜„์ƒ์—_๋Œ€ํ•œ_์ธ์‹_20220323 | 14 | 2,423 | 2,437 | | ํ•œ๊ตญ์–ธ๋ก ์ง„ํฅ์žฌ๋‹จ_๋ฏธ๋””์–ด์ด์Šˆ_์ฝ”๋กœ๋‚˜19_๊ด€๋ จ_์ •๋ณด_์ด์šฉ_๋ฐ_์ธ์‹_ํ˜„ํ™ฉ_20200326 | 19 | 2,360 | 2,379 | | ํ•œ๊ตญ๋ฌด์—ญ๋ณดํ—˜๊ณต์‚ฌ_ํ•ด์™ธ์‹œ์žฅ_์‹ ์šฉ์œ„ํ—˜_๋ณด๊ณ ์„œ_20240510 | 17 | 2,361 | 2,378 | | ๊ณผํ•™๊ธฐ์ˆ ์ •๋ณดํ†ต์‹ ๋ถ€_๊ตญ๋ฆฝ์ „ํŒŒ์—ฐ๊ตฌ์›_์ „์žํŒŒ_ํก์ˆ˜์ „๋ ฅ๋ฐ€๋„_๋“ฑ_์ „์žํŒŒ_์ธ์ฒด๋…ธ์ถœ๋Ÿ‰_ํ‰๊ฐ€๊ธฐ์ˆ _์—ฐ๊ตฌ_20241231 | 251 | 2,066 | 2,317 | | 2025๋…„_4๋ถ„๊ธฐ_์œ„์„ฑ์ •๋ณด_์„œ๋น„์Šค_ํ˜„ํ™ฉ | 19 | 2,218 | 2,237 | | ๋†์ง€๊ฐœ๋Ÿ‰ํ–‰์œ„์‹ ๊ณ ์—…๋ฌด์ง€์นจ | 24 | 2,175 | 2,199 | | ๊ทธ๋žœ๋“œ์ฝ”๋ฆฌ์•„๋ ˆ์ €(์ฃผ)_๋ธ”๋ž™์žญ_๊ฒŒ์ž„์˜_์ดํ•ด_20250617 | 38 | 1,777 | 1,815 | | **Total** | **19,621** | **127,131** | **146,752** | ### Query Type | Query Type | Count | |------------|-------| | Compare-Contrast | 22,211 | | Numerical | 21,774 | | Extractive | 21,243 | | Multi-Hop | 20,726 | | Enumerative | 20,382 | | Open-Ended | 20,317 | | Boolean | 20,099 | ### Query Format | Query Format | Count | |--------------|-------| | Instruction | 62,264 | | Question | 60,145 | | Keyword | 24,343 | | **Total** | **146,752** | ## Dataset Structure Each row represents a query-page pair with the following fields: ```json { "query_id": , "source_type": , "query_type": , "query_format": , "query": , "doc_id": , "image_id": , "image": , "markdown": , "elements": , "page_number_in_doc": } ``` - **query_id** \ : A unique numerical identifier for the query. - **source_type** \ : `"summary"` or `"context"`, metadata about the type of information used by the annotation pipeline to create the query. - **query_type** \ : The type of query (e.g., `"compare-contrast"`, `"open-ended"`, `"enumerative"`, `"multi-hop"`, `"extractive"`, `"numerical"`, `"boolean"`). - **query_format** \ : The syntactic format of the query (`"instruction"`, `"question"`, or `"keyword"`). - **query** \ : The actual text of the search question or instruction used for retrieval. - **doc_id** \ : Name of the source document. - **image_id** \ : A unique numerical identifier for the matched page. - **image** \ : The matched page image. - **markdown** \ : Extracted text from the page using an OCR pipeline. - **elements** \ : JSON-serialized list of extracted layout elements with bounding boxes and text from the page using an OCR pipeline. - **page_number_in_doc** \ : Original page number inside the document. ## License Information All annotations, query-document relevance judgments (qrels), and related metadata generated for this corpus are distributed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). The licensing status of the original source documents (the corpus) and any parsed text (`markdown` column in the corpus) are inherited from their respective publishers. For detailed metadata of each source document (title, doc_id, page count, URL, and license), refer to [`document_metadata.csv`](./document_metadata.csv). For documents subject to the [Korea Open Government License (KOGL)](https://www.kogl.or.kr/info/license.do) Type 1, the sources are attributed as follows: | Title | Doc ID | Type | Attribution Text | | :--- | :--- | :--- | :--- | | ๊ฐœ์ธ์ •๋ณด ์œ ์ถœ ๋“ฑ ์‚ฌ๊ณ  ๋Œ€์‘ ๋งค๋‰ด์–ผ | ๊ฐœ์ธ์ •๋ณด_์œ ์ถœ_๋“ฑ_์‚ฌ๊ณ _๋Œ€์‘_๋งค๋‰ด์–ผ(2024.3) | Type 1 | ๋ณธ ์ €์ž‘๋ฌผ์€ ๊ฐœ์ธ์ •๋ณด๋ณดํ˜ธ์œ„์›ํšŒ์—์„œ 2024๋…„ ์ž‘์„ฑํ•˜์—ฌ ๊ณต๊ณต๋ˆ„๋ฆฌ ์ œ 1์œ ํ˜•์œผ๋กœ ๊ฐœ๋ฐฉํ•œ '๊ฐœ์ธ์ •๋ณด ์œ ์ถœ ๋“ฑ ์‚ฌ๊ณ  ๋Œ€์‘ ๋งค๋‰ด์–ผ'์„ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ํ•ด๋‹น ์ €์ž‘๋ฌผ์€ [๊ฐœ์ธ์ •๋ณด๋ณดํ˜ธ์œ„์›ํšŒ ๋ฐœ๊ฐ„์ž๋ฃŒ](https://www.pipc.go.kr/np/cop/bbs/selectBoardArticle.do?bbsId=BS217&mCode=G010030000&nttId=10123)์—์„œ ๋ฌด๋ฃŒ๋กœ ๋‹ค์šด๋ฐ›์œผ์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. | | ์ƒ์ฒด์ •๋ณด ๋ณดํ˜ธ ์•ˆ๋‚ด์„œ | ์ƒ์ฒด์ •๋ณด_๋ณดํ˜ธ_์•ˆ๋‚ด์„œ(2024.12) | Type 1 | ๋ณธ ์ €์ž‘๋ฌผ์€ ๊ฐœ์ธ์ •๋ณด๋ณดํ˜ธ์œ„์›ํšŒ์—์„œ 2024๋…„ ์ž‘์„ฑํ•˜์—ฌ ๊ณต๊ณต๋ˆ„๋ฆฌ ์ œ 1์œ ํ˜•์œผ๋กœ ๊ฐœ๋ฐฉํ•œ '์ƒ์ฒด์ •๋ณด ๋ณดํ˜ธ ์•ˆ๋‚ด์„œ'๋ฅผ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ํ•ด๋‹น ์ €์ž‘๋ฌผ์€ [๊ฐœ์ธ์ •๋ณด๋ณดํ˜ธ์œ„์›ํšŒ ๋ฐœ๊ฐ„์ž๋ฃŒ](https://www.pipc.go.kr/np/cop/bbs/selectBoardArticle.do?bbsId=BS217&mCode=G010030000&nttId=10900)์—์„œ ๋ฌด๋ฃŒ๋กœ ๋‹ค์šด๋ฐ›์œผ์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. | | ํ•ฉ์„ฑ๋ฐ์ดํ„ฐ ์ƒ์„ฑํ™œ์šฉ ์•ˆ๋‚ด์„œ | ํ•ฉ์„ฑ๋ฐ์ดํ„ฐ_์ƒ์„ฑํ™œ์šฉ_์•ˆ๋‚ด์„œ(2024.12) | Type 1 | ๋ณธ ์ €์ž‘๋ฌผ์€ ๊ฐœ์ธ์ •๋ณด๋ณดํ˜ธ์œ„์›ํšŒ์—์„œ 2025๋…„ ์ž‘์„ฑํ•˜์—ฌ ๊ณต๊ณต๋ˆ„๋ฆฌ ์ œ 1์œ ํ˜•์œผ๋กœ ๊ฐœ๋ฐฉํ•œ 'ํ•ฉ์„ฑ๋ฐ์ดํ„ฐ ์ƒ์„ฑยทํ™œ์šฉ ์•ˆ๋‚ด์„œ'๋ฅผ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ํ•ด๋‹น ์ €์ž‘๋ฌผ์€ [๊ฐœ์ธ์ •๋ณด๋ณดํ˜ธ์œ„์›ํšŒ ๋ฐœ๊ฐ„์ž๋ฃŒ](https://www.pipc.go.kr/np/cop/bbs/selectBoardArticle.do?bbsId=BS217&mCode=G010030000&nttId=10915)์—์„œ ๋ฌด๋ฃŒ๋กœ ๋‹ค์šด๋ฐ›์œผ์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. | | ํ•ด์–‘์ˆ˜์‚ฐ๋ถ€ ๋ฌด์ธ๋„์„œ 100์„  | ํ•ด์–‘์ˆ˜์‚ฐ๋ถ€_๋ฌด์ธ๋„์„œ_100์„ _20221231 | Type 1 | ๋ณธ ์ €์ž‘๋ฌผ์€ ํ•ด์–‘์ˆ˜์‚ฐ๋ถ€์—์„œ 2021๋…„ ์ž‘์„ฑํ•˜์—ฌ ๊ณต๊ณต๋ˆ„๋ฆฌ ์ œ 1์œ ํ˜•์œผ๋กœ ๊ฐœ๋ฐฉํ•œ '๋ฌด์ธ๋„์„œ ๋ฐฑ์„œ'๋ฅผ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ํ•ด๋‹น ์ €์ž‘๋ฌผ์€ [ํ•ด์–‘์ˆ˜์‚ฐ๋ถ€ ๋ฌด์ธ๋„์„œ์ข…ํ•ฉ์ •๋ณด์‹œ์Šคํ…œ](http://uii.mof.go.kr)"์—์„œ ๋ฌด๋ฃŒ๋กœ ๋‹ค์šด๋ฐ›์œผ์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. | | ์œ„์„ฑ์ •๋ณด ์„œ๋น„์Šค ํ˜„ํ™ฉ | 2025๋…„_4๋ถ„๊ธฐ_์œ„์„ฑ์ •๋ณด_์„œ๋น„์Šค_ํ˜„ํ™ฉ | Type 1 | ๋ณธ ์ €์ž‘๋ฌผ์€ ๊ณผํ•™๊ธฐ์ˆ ์ •๋ณดํ†ต์‹ ๋ถ€ ์ค‘์•™์ „ํŒŒ๊ด€๋ฆฌ์†Œ์—์„œ 2026๋…„ ์ž‘์„ฑํ•˜์—ฌ ๊ณต๊ณต๋ˆ„๋ฆฌ ์ œ 1์œ ํ˜•์œผ๋กœ ๊ฐœ๋ฐฉํ•œ '2025๋…„ 4๋ถ„๊ธฐ ์œ„์„ฑ์ „ํŒŒ ๊ฐ์‹œ๋™ํ–ฅ'์„ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ํ•ด๋‹น ์ €์ž‘๋ฌผ์€ [์ค‘์•™์ „ํŒŒ๊ด€๋ฆฌ์†Œ ์œ„์„ฑ์ „ํŒŒ๊น€์‹œ์„ผํ„ฐ ์œ„์„ฑ์ „ํŒŒ๊ฐ์‹œ์ •๋ณด](https://www.srmc.go.kr/obser/obserInfoBBSList.do)'์—์„œ ๋ฌด๋ฃŒ๋กœ ๋‹ค์šด๋ฐ›์œผ์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. | | ํ•œ๊ตญ์›์ž๋ ฅํ™˜๊ฒฝ๊ณต๋‹จ ์ฒ˜๋ถ„์‹œ์„ค ๋ถ€์ง€์ฃผ๋ณ€ ๋ฐฉ์‚ฌ์„ ํ™˜๊ฒฝ์กฐ์‚ฌ ๋ณด๊ณ ์„œ | ํ•œ๊ตญ์›์ž๋ ฅํ™˜๊ฒฝ๊ณต๋‹จ_์ฒ˜๋ถ„์‹œ์„ค_๋ถ€์ง€์ฃผ๋ณ€_๋ฐฉ์‚ฌ์„ ํ™˜๊ฒฝ์กฐ์‚ฌ_๋ณด๊ณ ์„œ_20250831 | Type 1 | ๋ณธ ์ €์ž‘๋ฌผ์€ ํ•œ๊ตญ์›์ž๋ ฅํ™˜๊ฒฝ๊ณต๋‹จ์—์„œ 2025๋…„ ์ž‘์„ฑํ•˜์—ฌ ๊ณต๊ณต๋ˆ„๋ฆฌ ์ œ 1์œ ํ˜•์œผ๋กœ ๊ฐœ๋ฐฉํ•œ '๋ฐฉ์‚ฌ์„ฑํ๊ธฐ๋ฌผ ์ฒ˜๋ถ„์‹œ์„ค ๋ถ€์ง€์ฃผ๋ณ€ ๋ฐฉ์‚ฌ์„ ํ™˜๊ฒฝ๋ณด๊ณ ์„œ'๋ฅผ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ํ•ด๋‹น ์ €์ž‘๋ฌผ์€ [๊ณต๊ณต๋ฐ์ดํ„ฐํฌํ„ธ](https://www.data.go.kr/data/15156699/fileData.do)์—์„œ ๋ฌด๋ฃŒ๋กœ ๋‹ค์šด๋ฐ›์œผ์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. | | ํ•œ๊ตญ์–ธ๋ก ์ง„ํฅ์žฌ๋‹จ ๋ฏธ๋””์–ด์ด์Šˆ ์ด๋Œ€๋‚จ ํ˜„์ƒ์— ๋Œ€ํ•œ ์ธ์‹ | ํ•œ๊ตญ์–ธ๋ก ์ง„ํฅ์žฌ๋‹จ_๋ฏธ๋””์–ด์ด์Šˆ_์ด๋Œ€๋‚จ_ํ˜„์ƒ์—_๋Œ€ํ•œ_์ธ์‹_20220323 | Type 1 | ๋ณธ ์ €์ž‘๋ฌผ์€ ํ•œ๊ตญ์–ธ๋ก ์ง„ํฅ์žฌ๋‹จ์—์„œ 2022๋…„ ์ž‘์„ฑํ•˜์—ฌ ๊ณต๊ณต๋ˆ„๋ฆฌ ์ œ 1์œ ํ˜•์œผ๋กœ ๊ฐœ๋ฐฉํ•œ '์ด๋Œ€๋‚จ ํ˜„์ƒ์— ๋Œ€ํ•œ ์ธ์‹'์„ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ํ•ด๋‹น ์ €์ž‘๋ฌผ์€ [๊ณต๊ณต๋ฐ์ดํ„ฐํฌํ„ธ](https://www.data.go.kr/data/15112343/fileData.do)์—์„œ ๋ฌด๋ฃŒ๋กœ ๋‹ค์šด๋ฐ›์œผ์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. | | ํ•œ๊ตญ์–ธ๋ก ์ง„ํฅ์žฌ๋‹จ ๋ฏธ๋””์–ด์ด์Šˆ ์ฝ”๋กœ๋‚˜19 ๊ด€๋ จ ์ •๋ณด ์ด์šฉ ๋ฐ ์ธ์‹ ํ˜„ํ™ฉ | ํ•œ๊ตญ์–ธ๋ก ์ง„ํฅ์žฌ๋‹จ_๋ฏธ๋””์–ด์ด์Šˆ_์ฝ”๋กœ๋‚˜19_๊ด€๋ จ_์ •๋ณด_์ด์šฉ_๋ฐ_์ธ์‹_ํ˜„ํ™ฉ_20200326 | Type 1 | ๋ณธ ์ €์ž‘๋ฌผ์€ ํ•œ๊ตญ์–ธ๋ก ์ง„ํฅ์žฌ๋‹จ์—์„œ 2020๋…„ ์ž‘์„ฑํ•˜์—ฌ ๊ณต๊ณต๋ˆ„๋ฆฌ ์ œ 1์œ ํ˜•์œผ๋กœ ๊ฐœ๋ฐฉํ•œ '์ฝ”๋กœ๋‚˜19(COVID-19) ๊ด€๋ จ ์ •๋ณด ์ด์šฉ ๋ฐ ์ธ์‹ ํ˜„ํ™ฉ'์„ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ํ•ด๋‹น ์ €์ž‘๋ฌผ์€ [๊ณต๊ณต๋ฐ์ดํ„ฐํฌํ„ธ](https://www.data.go.kr/data/15086396/fileData.do)์—์„œ ๋ฌด๋ฃŒ๋กœ ๋‹ค์šด๋ฐ›์œผ์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. | | ํ•œ๊ตญ์–ธ๋ก ์ง„ํฅ์žฌ๋‹จ ๋ฏธ๋””์–ด์ด์Šˆ ๊ด‘๊ณ ์š”๊ธˆ์ œ ๋„์ž…์„ ์•ž๋‘” ๋„ทํ”Œ๋ฆญ์Šค์— ๋Œ€ํ•œ ์ธ์‹ ๋ฐ ์ด์šฉ ์กฐ์‚ฌ | ํ•œ๊ตญ์–ธ๋ก ์ง„ํฅ์žฌ๋‹จ_๋ฏธ๋””์–ด์ด์Šˆ_๊ด‘๊ณ ์š”๊ธˆ์ œ_๋„์ž…์„_์•ž๋‘”_๋„ทํ”Œ๋ฆญ์Šค์—_๋Œ€ํ•œ_์ธ์‹_๋ฐ_์ด์šฉ_์กฐ์‚ฌ_20220928 | Type 1 | ๋ณธ ์ €์ž‘๋ฌผ์€ ํ•œ๊ตญ์–ธ๋ก ์ง„ํฅ์žฌ๋‹จ์—์„œ 2022๋…„ ์ž‘์„ฑํ•˜์—ฌ ๊ณต๊ณต๋ˆ„๋ฆฌ ์ œ 1์œ ํ˜•์œผ๋กœ ๊ฐœ๋ฐฉํ•œ '๊ด‘๊ณ ์š”๊ธˆ์ œ ๋„์ž…์„ ์•ž๋‘” ๋„ทํ”Œ๋ฆญ์Šค์— ๋Œ€ํ•œ ์ธ์‹ ๋ฐ ์ด์šฉ ์กฐ์‚ฌ'๋ฅผ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ํ•ด๋‹น ์ €์ž‘๋ฌผ์€ [๊ณต๊ณต๋ฐ์ดํ„ฐํฌํ„ธ](https://www.data.go.kr/data/15112345/fileData.do)์—์„œ ๋ฌด๋ฃŒ๋กœ ๋‹ค์šด๋ฐ›์œผ์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. | | ๊ณผํ•™๊ธฐ์ˆ ์ •๋ณดํ†ต์‹ ๋ถ€ ๊ตญ๋ฆฝ์ „ํŒŒ์—ฐ๊ตฌ์› ์ „์žํŒŒ ํก์ˆ˜์ „๋ ฅ๋ฐ€๋„ ๋“ฑ ์ „์žํŒŒ ์ธ์ฒด๋…ธ์ถœ๋Ÿ‰ ํ‰๊ฐ€๊ธฐ์ˆ  ์—ฐ๊ตฌ | ๊ณผํ•™๊ธฐ์ˆ ์ •๋ณดํ†ต์‹ ๋ถ€_๊ตญ๋ฆฝ์ „ํŒŒ์—ฐ๊ตฌ์›_์ „์žํŒŒ_ํก์ˆ˜์ „๋ ฅ๋ฐ€๋„_๋“ฑ_์ „์žํŒŒ_์ธ์ฒด๋…ธ์ถœ๋Ÿ‰_ํ‰๊ฐ€๊ธฐ์ˆ _์—ฐ๊ตฌ_20241231 | Type 1 | ๋ณธ ์ €์ž‘๋ฌผ์€ ๊ณผํ•™๊ธฐ์ˆ ์ •๋ณดํ†ต์‹ ๋ถ€ ๊ตญ๋ฆฝ์ „ํŒŒ์—ฐ๊ตฌ์›์—์„œ 2024๋…„ ์ž‘์„ฑํ•˜์—ฌ ๊ณต๊ณต๋ˆ„๋ฆฌ ์ œ 1์œ ํ˜•์œผ๋กœ ๊ฐœ๋ฐฉํ•œ '์ „์žํŒŒ ํก์ˆ˜์ „๋ ฅ ๋ฐ€๋„ ๋“ฑ ์ „์žํŒŒ ์ธ์ฒด๋…ธ์ถœ๋Ÿ‰ ํ‰๊ฐ€๊ธฐ์ˆ  ์—ฐ๊ตฌ'๋ฅผ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ํ•ด๋‹น ์ €์ž‘๋ฌผ์€ [๊ณต๊ณต๋ฐ์ดํ„ฐํฌํ„ธ](https://www.data.go.kr/data/15112345/fileData.do)์—์„œ ๋ฌด๋ฃŒ๋กœ ๋‹ค์šด๋ฐ›์œผ์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. | | ๊ณผํ•™๊ธฐ์ˆ ์ •๋ณดํ†ต์‹ ๋ถ€ ๊ตญ๋ฆฝ์ „ํŒŒ์—ฐ๊ตฌ์› ICT ์œต๋ณตํ•ฉ ์‹œ์„ค์˜ ์•ˆ์ „ํ•œ ์ „์žํŒŒ ํ™˜๊ฒฝ ๊ธฐ๋ฐ˜ ์กฐ์„ฑ ์—ฐ๊ตฌ | ๊ณผํ•™๊ธฐ์ˆ ์ •๋ณดํ†ต์‹ ๋ถ€_๊ตญ๋ฆฝ์ „ํŒŒ์—ฐ๊ตฌ์›_ICT_์œต๋ณตํ•ฉ_์‹œ์„ค์˜_์•ˆ์ „ํ•œ_์ „์žํŒŒ_ํ™˜๊ฒฝ_๊ธฐ๋ฐ˜_์กฐ์„ฑ_์—ฐ๊ตฌ_20241231 | Type 1 | ๋ณธ ์ €์ž‘๋ฌผ์€ ๊ณผํ•™๊ธฐ์ˆ ์ •๋ณดํ†ต์‹ ๋ถ€ ๊ตญ๋ฆฝ์ „ํŒŒ์—ฐ๊ตฌ์›์—์„œ 2024๋…„ ์ž‘์„ฑํ•˜์—ฌ ๊ณต๊ณต๋ˆ„๋ฆฌ ์ œ 1์œ ํ˜•์œผ๋กœ ๊ฐœ๋ฐฉํ•œ 'ICT ์œตใƒป๋ณตํ•ฉ ์‹œ์„ค์˜ ์•ˆ์ „ํ•œ ์ „์žํŒŒ ํ™˜๊ฒฝ ๊ธฐ๋ฐ˜ ์กฐ์„ฑ ์—ฐ๊ตฌ'๋ฅผ ์ด์šฉํ•˜์˜€์œผ๋ฉฐ, ํ•ด๋‹น ์ €์ž‘๋ฌผ์€ [๊ณต๊ณต๋ฐ์ดํ„ฐํฌํ„ธ](https://www.data.go.kr/data/15145080/fileData.do)์—์„œ ๋ฌด๋ฃŒ๋กœ ๋‹ค์šด๋ฐ›์œผ์‹ค ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. | ## Acknowledgements This dataset was generated using the [kovidore-data-generator](https://github.com/whybe-choi/kovidore-data-generator) pipeline. We acknowledge the datasets provided by the [Public Data Portal(๊ณต๊ณต๋ฐ์ดํ„ฐํฌํ„ธ)](https://www.data.go.kr/index.do), which were utilized to construct this training dataset. ## Citation If you use Ko-VDR Train Public in your research, please cite as follows: ```bibtex @inproceedings{choi-etal-2026-kovidore, title = "{KoViDoRe: A Benchmark for {K}orean Visual Document Retrieval", author = "Choi, Yongbin and Song, Yongwoo and Sung, Mujeen", booktitle = "Proceedings of the 2nd Workshop on Multimodal Augmented Generation via Multimodal Retrieval (MAGMaR 2026)", year = "2026", doi = "10.18653/v1/2026.magmar-main.11", url = "https://aclanthology.org/2026.magmar-main.11/" } ```