README.md

---
license: cc-by-4.0
task_categories:
- visual-question-answering
- image-to-text
language:
- en
tags:
- chart-qa
- synthetic
- multimodal
- data-visualization
- vlm
pretty_name: Multi-Domain Synthetic Chart QA (Verifiable Labels)
size_categories:
- 10K<n<100K
---

# Multi-Domain Synthetic Chart QA (Verifiable Labels)

A synthetic **chart question-answering** dataset for training and evaluating
Vision-Language Models (VLMs). Each example is a rendered chart image paired with
a natural-language question and an answer that is **computed from the underlying
data**, so every label is ground truth by construction — not a language model's
guess.

Generated by the pipeline at
[Code-based-Synthetic-Multimodal-Data-Generation](https://github.com/dantelok/Code-based-Synthetic-Multimodal-Data-Generation).

**Reference build:** ~12,600 QA pairs over ~1,800 charts, balanced across three
domains (climate / e-commerce / housing) and four chart types (bar, pie, line,
scatter), split 90/10 train/test. Rebuild it deterministically with
`build_hf_dataset.py --charts-per-domain 600 --seed 0`.

## Why this dataset

Most chart-QA data is scraped or has answers written by an LLM, which cannot
guarantee correctness — exactly where it matters most (aggregations). Here the
chart and the QA are produced from the **same exact data slice**: the chart is
rendered deterministically with matplotlib, and the answer is computed with
pandas. For a column `cases = [100, 250, 400]`, "total" is **750** and "max" is
**400**, never a plausible-but-wrong value. This mirrors how DVQA, FigureQA, and
PlotQA are constructed.

## How it was built

For each source table the builder repeatedly:
1. picks a chart type (bar / pie / line / scatter) and columns,
2. aggregates an exact slice (e.g. total of a numeric column grouped by a category),
3. renders the chart deterministically, and
4. generates ground-truth QA over that same slice
   (`sum, mean, max, min, range, argmax, argmin, count, compare, lookup`).

The whole process is offline, seeded, and reproducible — no API calls. See
`build_hf_dataset.py` in the source repo.

## Data fields

| Field | Type | Description |
|---|---|---|
| `image` | image | The rendered chart (PNG) |
| `question` | string | A natural-language question about the chart |
| `answer` | string | The ground-truth answer, computed from the data |
| `chart_type` | string | `bar` / `pie` / `line` / `scatter` |
| `domain` | string | `climate` / `ecommerce` / `housing` |
| `source` | string | Upstream dataset + license |
| `op` | string | Operation used to compute the answer (e.g. `argmax`) |
| `columns` | list[string] | Columns involved in the question |

## Splits

`train` / `test` (90 / 10), split with a fixed seed.

## Baseline

A strong off-the-shelf VLM (Cohere `command-a-vision-07-2025`), zero-shot on a
120-example test sample: **79%** on structural questions (which category /
comparison / count), **22%** on questions requiring an exact numeric value,
**40%** overall. The gap reflects a known weakness of current VLMs — reading
chart structure is easier than reading or computing precise values — which is
exactly what verifiable labels let you measure. Reproduce with
`evaluate_baseline.py` in the [source repo](https://github.com/dantelok/Code-based-Synthetic-Multimodal-Data-Generation).

## Source data & attribution

All sources are Creative Commons Attribution 4.0 (CC BY 4.0); this derived
dataset is released under the same license.

- **Climate** — *Our World in Data, CO₂ and Greenhouse Gas Emissions* — CC BY 4.0
  — https://github.com/owid/co2-data
- **E-commerce** — *UCI Machine Learning Repository, Online Retail* — CC BY 4.0
  — https://archive.ics.uci.edu/dataset/352/online+retail
- **Housing** — *Inside Airbnb, New York City listings* — CC BY 4.0 — https://insideairbnb.com/get-the-data/

## Limitations

- **Read-off precision.** Scatter/line answers (e.g. an exact maximum) may not be
  perfectly readable from the image alone; they are exact for the underlying slice.
- **Template phrasing.** Questions come from templates, so linguistic diversity is
  narrower than human-written questions (answers are what's guaranteed, not fluency).
- **Aggregation choices.** Bar/pie answers are over *summed* groups (as plotted);
  the `op`/`columns` metadata makes the computation explicit and auditable.
- **Source snapshots.** Values reflect the source files at build time.

## Citation

```bibtex
@misc{synthetic_chart_qa,
  title  = {Multi-Domain Synthetic Chart QA (Verifiable Labels)},
  author = {Lok, Dante and Kaur, Avneet and Chagas Fernandes, Reuben},
  year   = {2025},
  howpublished = {\url{https://github.com/dantelok/Code-based-Synthetic-Multimodal-Data-Generation}}
}
```

## License

CC BY 4.0. You must credit this dataset and the upstream sources listed above.