Datasets:
Tasks:
Question Answering
Formats:
json
Sub-tasks:
multiple-choice-qa
Languages:
Korean
Size:
1K - 10K
ArXiv:
License:
| pretty_name: K-MetBench | |
| language: | |
| - ko | |
| license: cc-by-nc-sa-4.0 | |
| task_categories: | |
| - question-answering | |
| task_ids: | |
| - multiple-choice-qa | |
| size_categories: | |
| - 1K<n<10K | |
| tags: | |
| - meteorology | |
| - korean | |
| - multimodal | |
| - reasoning | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: test | |
| path: data/kmetbench.json | |
| # K-MetBench: A Multi-Dimensional Benchmark for Fine-Grained Evaluation of Expert Reasoning, Locality, and Multimodality in Meteorology | |
| [](http://arxiv.org/abs/2604.24645) [](https://kmetbench.github.io) [](https://github.com/kmetbench/kmetbench-release) [](#citation) | |
| K-MetBench is a multi-dimensional benchmark for evaluating meteorology models across | |
| accuracy, reasoning quality, geo-cultural alignment, and fine-grained domain coverage. | |
| The public eval protocol uses only the explicit advanced benchmark and the explicit reasoning benchmark followed by LLM-as-a-judge evaluation. | |
| The implicit split may be distributed with the dataset, but it is not part of the public eval kit. | |
| ## Dataset Summary | |
| - Total Questions: 1774 | |
| - Total Image References: 151 (59 question images, 92 choice images) | |
| - Modality Split: text-only 1692, multimodal 82 | |
| - Reasoning Subset: 141 | |
| - Geo-Cultural Subset: 73 | |
| - Parts: Part 1: 373, Part 2: 332, Part 3: 359, Part 4: 376, Part 5: 334 | |
| - Format: JSON file with relative image paths under `data/images/` | |
| ## Data Format | |
| Each sample contains: | |
| | Field | Type | Description | | |
| | --- | --- | --- | | |
| | `id` | int | Stable item identifier | | |
| | `question.text` | string | Question text | | |
| | `question.image` | string | Relative path to a question image, if present | | |
| | `choices[].text` | string | Choice text | | |
| | `choices[].image` | string | Relative path to a choice image, if present | | |
| | `answer` | int | Zero-based correct choice index | | |
| | `source` | string | Exam session source tag | | |
| | `source_id` | int | Original source-local item id | | |
| | `rationale` | string | Expert-verified reasoning text when available | | |
| | `korean` | bool | Geo-cultural subset flag | | |
| | `multimodal` | bool | Multimodal subset flag | | |
| | `part` | int | Official part number (1-5) | | |
| | `category` | object | Subject/topic metadata | | |
| ## Usage | |
| ### Loading the Dataset | |
| ```python | |
| from datasets import load_dataset | |
| dataset = load_dataset( | |
| "json", | |
| data_files="https://huggingface.co/datasets/soyeonbot/K-MetBench/resolve/main/data/kmetbench.json", | |
| split="test", | |
| ) | |
| sample = dataset[0] | |
| print(sample["question"]["text"]) | |
| print(sample["answer"]) | |
| ``` | |
| ### Viewing Referenced Images | |
| ```python | |
| import requests | |
| from io import BytesIO | |
| from PIL import Image | |
| image_rel_path = sample["question"]["image"] | |
| image_url = "https://huggingface.co/datasets/soyeonbot/K-MetBench/resolve/main/data/images/" + image_rel_path | |
| image = Image.open(BytesIO(requests.get(image_url, timeout=30).content)) | |
| image.show() | |
| ``` | |
| ### Running the Public Eval Kit | |
| ```bash | |
| pip install -r requirements-eval.txt | |
| python scripts/eval.py run --list-model-configs | |
| python scripts/eval.py run --model-config <model_config> --prompt-type advanced --explicit-data-file data/kmetbench.json --image-root data/images | |
| python scripts/eval.py run --model-config <model_config> --prompt-type reasoning --explicit-data-file data/kmetbench.json --image-root data/images | |
| python scripts/eval.py judge --model <model> --predictions <explicit_reasoning_json> --explicit-data-file data/kmetbench.json | |
| ``` | |
| ## License | |
| This dataset is released under CC BY-NC-SA 4.0. | |
| ## Contact | |
| For questions about the dataset, contact Soyeon Kim (soyeon.k@kaist.ac.kr). | |
| ## Citation | |
| ```bibtex | |
| @inproceedings{kim2026kmetbench, | |
| title = {K-MetBench: A Multi-Dimensional Benchmark for Fine-Grained Evaluation of Expert Reasoning, Locality, and Multimodality in Meteorology}, | |
| author = {Kim, Soyeon and Kang, Cheongwoong and Lee, Myeongjin and Chang, Eun-Chul and Lee, Jaedeok and Choi, Jaesik}, | |
| booktitle = {Findings of the Association for Computational Linguistics: ACL 2026}, | |
| year = {2026}, | |
| url = {http://arxiv.org/abs/2604.24645} | |
| } | |
| ``` | |