| --- |
| pretty_name: VisDoTQA |
| language: |
| - en |
| license: unknown |
| task_categories: |
| - visual-question-answering |
| - question-answering |
| size_categories: |
| - 1K<n<10K |
| source_datasets: |
| - original |
| annotations_creators: |
| - machine-generated |
| language_creators: |
| - machine-generated |
| multilinguality: |
| - monolingual |
| tags: |
| - chart |
| - chart-understanding |
| - multimodal |
| - vision-language |
| - reasoning |
| - synthetic |
| - benchmark |
| --- |
| |
| # VisDoTQA: Enhancing Visual Reasoning through Human-Like Interpretation Grounding and Decomposition of Thought |
|
|
| This repository releases **VisDoTQA**, the public benchmark introduced in our paper [*VisDoT : Enhancing Visual Reasoning through Human-Like Interpretation Grounding and Decomposition of Thought*](https://aclanthology.org/2026.findings-eacl.30/). |
|
|
| The canonical publication record is the **ACL Anthology** page for **Findings of the Association for Computational Linguistics: EACL 2026**, and an additional mirror is available on **arXiv:2603.11631**. |
| See our [GitHub repository](https://github.com/bongdong22/VisDoTQA) for the synchronized source release and updates. |
| See the paper on [ACL Anthology](https://aclanthology.org/2026.findings-eacl.30/), on [arXiv:2603.11631](https://arxiv.org/abs/2603.11631), or via [DOI](https://doi.org/10.18653/v1/2026.findings-eacl.30). |
|
|
| ## Highlights |
|
|
| - We release **VisDoTQA**, a public benchmark for evaluating **visual grounding** and **compositional reasoning** on chart images. |
| - The benchmark contains **1,120 QA pairs** built from **609 held-out charts**. |
| - VisDoTQA covers four perceptual task families: **Position**, **Length**, **Pattern**, and **Extract**. |
| - This Hugging Face repository releases the **public benchmark test split only**. The full research dataset described in the paper contains **331,969 QA pairs** and is not included here. |
|
|
| ## Dataset Structure |
|
|
| - **Split**: `test` |
| - **Images**: `test/images/` |
| - **Metadata**: `test/metadata.jsonl` |
|
|
| Each example contains: |
|
|
| - `file_name`: relative path to the chart image |
| - `imgname`: image filename |
| - `query`: benchmark question |
| - `label`: ground-truth answer |
| - `source`: VisDoTQA task category (`Position`, `Length`, `Pattern`, `Extract`) |
|
|
| ## Links |
|
|
| - [Paper (ACL Anthology, canonical)](https://aclanthology.org/2026.findings-eacl.30/) |
| - [Paper (arXiv:2603.11631 mirror)](https://arxiv.org/abs/2603.11631) |
| - [DOI](https://doi.org/10.18653/v1/2026.findings-eacl.30) |
| - [GitHub Repository](https://github.com/bongdong22/VisDoTQA) |
|
|
| ## Contact |
|
|
| If you have questions about this dataset release, please use the [GitHub repository](https://github.com/bongdong22/VisDoTQA). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @inproceedings{lee2026visdot, |
| title={VisDoT : Enhancing Visual Reasoning through Human-Like Interpretation Grounding and Decomposition of Thought}, |
| author={Lee, Eunsoo and Lee, Jeongwoo and Hong, Minki and Choi, Jangho and Kim, Jihie}, |
| booktitle={Findings of the Association for Computational Linguistics: EACL 2026}, |
| pages={610--640}, |
| year={2026}, |
| doi={10.18653/v1/2026.findings-eacl.30}, |
| url={https://aclanthology.org/2026.findings-eacl.30/} |
| } |
| ``` |
|
|
