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---
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license: apache-2.0
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---
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# What Lies Beneath: A Call for Distribution-based Visual Question & Answer Datasets
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Publication: TBD
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GitHub Repo: TBD
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This is a histogram-based dataset for visual question and answer (VQA) with humans and large language/multimodal models (LMMs).
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Data contains synthetically generated single-panel histograms images, data used to create histograms, bounding box data for titles, axis and tick labels, and data marks, and VQA question-answer pairs. The subset of data presented in the paper (`example_hist/` folder) includes both human (two annotators) and LMM (ChatGPT-5-nano) annotations.
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See GitHub link for code used to create and parse the following files.
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## Directory Structure
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Overview of the [directory structure](https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/tree/main) is as follows:
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- `example_hists/` -- contains img and json for a small (80 images), visually uniform set of histogram data with several questions annotated by both LMMs
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- `example_hists_larger/` -- larger (500 images) dataset of uniform histogram images
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- `example_hists_complex/` -- largest (100 images) dataset of histograms with a variety of distributions, shapes, colors, etc.
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Paper-dataset (`example_hists/`) [directory structure](https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/tree/main/example_hists):
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- `LLM_outputs/` -- contains outputs from various trials using ChatGPT-5
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- `imgs/` -- stores all images (also in `imgs.zip` file)
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- `jsons/` -- stores JSON for bounding boxes, data used to create images, VQA data
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- `human_and_llm_annotated_data.csv` -- contains two human annotations and two LMM annotations (gpt-5-nano, gpt-5-mini) for a subset of questions
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## Human and LMM Annotations
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## Citation information
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If you use this work please cite:
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```
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TBD
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``` |
