---
license: apache-2.0
---

# What Lies Beneath: A Call for Distribution-based Visual Question & Answer Datasets

Publication: TBD
GitHub Repo: TBD

This is a histogram-based dataset for visual question and answer (VQA) with humans and large language/multimodal models (LMMs).

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.

See GitHub link for code used to create and parse the following files.

## Directory Structure

Overview of the [directory structure](https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/tree/main) is as follows:
 - `example_hists/` -- contains img and json for a small (80 images), visually uniform set of histogram data with several questions annotated by both LMMs
 - `example_hists_larger/` -- larger (500 images) dataset of uniform histogram images
 - `example_hists_complex/` -- largest (100 images) dataset of histograms with a variety of distributions, shapes, colors, etc.

Paper-dataset (`example_hists/`) [directory structure](https://huggingface.co/datasets/ReadingTimeMachine/visual_qa_histograms/tree/main/example_hists):
 - `LLM_outputs/` -- contains outputs from various trials using ChatGPT-5
 - `imgs/` -- stores all images (also in `imgs.zip` file)
 - `jsons/` -- stores JSON for bounding boxes, data used to create images, VQA data
 - `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


## Human and LMM Annotations



## Citation information

If you use this work please cite:
```
TBD
```