README.md

---
language:
  - ru
  - en
license: other
license_name: mixed-research-use
size_categories:
  - 1K<n<10K
task_categories:
  - visual-question-answering
  - question-answering
task_ids:
  - visual-question-answering
pretty_name: RuChartQA
tags:
  - chart-understanding
  - visual-reasoning
  - russian
  - benchmark
  - vlm-evaluation
configs:
  - config_name: chartbasic
    data_files: synthetic/chartbasic.jsonl
  - config_name: chartreasoning
    data_files: synthetic/chartreasoning.jsonl
  - config_name: chartperception
    data_files: synthetic/chartperception.jsonl
  - config_name: chartreal
    data_files: chartreal/data.jsonl
---

# RuChartQA

A Russian-language chart question answering benchmark for evaluating Vision-Language Models, with both **synthetic** and **real-world** evaluation sets.

## Dataset summary

| Split | Examples | Charts | Source |
|---|---|---|---|
| Synthetic ChartBasic | 360 | 90 (×4 variants) | Generated |
| Synthetic ChartReasoning | 480 | 120 (×4 variants) | Generated |
| Synthetic ChartPerception | 360 | 90 (×4 variants) | Generated |
| ChartReal | 242 QA | 96 charts | Rosstat, Bank of Russia (PDF) |
| **Total** | **1442 QA** | **396 unique charts** | |

The synthetic split has 4 variants per chart: `ru_image`, `en_image`, `ru_text` (text description instead of image), `en_text` — enabling controlled language and modality ablations. ChartReal is `ru_image` only.

## Why this benchmark

Most chart-QA benchmarks (ChartQA, PlotQA, FigureQA) are English-only. Existing Russian-language chart evaluation has been limited to translated subsets. This benchmark addresses two gaps:

1. **Language coverage.** Native Russian questions, Russian-language axis labels, captions, and currencies (₽).
2. **Real-world distribution shift.** Synthetic-only benchmarks systematically overestimate VLM performance on real-world graphs from government statistics and central bank publications. Our analysis (see `results/leaderboard.csv` and the [accompanying paper]) shows performance gaps of **+11 to +41 percentage points** between synthetic and real-world splits across three modern VLMs.

## Loading

```python
from datasets import load_dataset

# Real-world split (the one with the bigger story)
chartreal = load_dataset("romath/RuChartQA", "chartreal")

# Synthetic splits
chartbasic = load_dataset("romath/RuChartQA", "chartbasic")
chartreasoning = load_dataset("romath/RuChartQA", "chartreasoning")
chartperception = load_dataset("romath/RuChartQA", "chartperception")
```

## Schema

Each row has:

| Field | Type | Description |
|---|---|---|
| `example_id` | string | Unique identifier (e.g. `chartreal_007_q2_ru_image`) |
| `subdataset` | string | `ChartBasic`, `ChartReasoning`, `ChartPerception`, or `ChartReal` |
| `variant` | string | `ru_image`, `en_image`, `ru_text`, `en_text` |
| `language` | string | `ru` or `en` |
| `modality` | string | `image` or `text` |
| `chart_type` | string | `bar`, `line`, `mixed`, `pie` |
| `chart_id` | string | Chart identifier (multiple QA may share one chart) |
| `question_type` | string | `lookup`, `comparison`, `min`, `max`, `difference`, `conditional` |
| `question` | string | Natural-language question |
| `answer` | string | Gold answer |
| `answer_numeric` | float \| null | Numeric form if applicable (for tolerance scoring) |
| `answer_type` | string | `numeric` or `categorical` |
| `image_path` | string \| null | Relative path to PNG (for image variants) |
| `text_description` | string \| null | Text description of the chart (for text variants) |

## Evaluation

We provide a normalizer (`eval/normalize.py`) that handles:

- Numeric tolerance (5%, the ChartQA standard) with a year-as-numeric exception requiring exact match (1900–2100)
- Bidirectional substring matching for categorical answers (`gold ⊆ pred` or `pred ⊆ gold`), disabled when gold contains compound markers (`и`, `or`, `,`)
- Lower/strip/punctuation normalization

Minimal example:

```bash
python3 eval/eval_example.py predictions.jsonl chartreal/data.jsonl
```

A prediction file is JSONL with `{"example_id": ..., "prediction_raw": "..."}` per line.

## Baselines

Predictions on ChartReal from four systems are included in `baselines/`:

| System | ChartReal Accuracy | Synthetic ru_image |
|---|---|---|
| Qwen3-VL 32B Instruct | **75.2%** | 86.3% |
| Gemini 2.5 Flash | 71.1% | 92.7% |
| Nemotron Nano 12B v2 VL | 45.9% | 86.7% |
| OCR + Llama 3.3 70B (text-only baseline) | 34.7% | n/a |

All gaps between systems on ChartReal are statistically significant (95% bootstrap CI) **except Qwen vs Gemini** (Δ=+4.1pp, CI [−1.2, +9.5], p=0.16). See `results/leaderboard.csv`.

## Construction

### Synthetic
Generated from category templates (cities, products, demographics, etc.) with controlled distributions over chart types (bar) and question types. Each chart was rendered in Russian and English; for each language, both an image and a text-description variant exist. This 4-way structure allows clean ablations of language and modality effects.

### ChartReal
Charts were extracted from public PDF reports of:
- **Rosstat** (Russian Federal State Statistics Service) — annual and monthly statistical bulletins
- **Bank of Russia (CBR)** — financial stability reports, monetary policy commentary

Each chart received 1–4 questions covering different reasoning types. Charts span four types (bar, line, mixed, pie) with realistic noise: small fonts, dense legends, multi-axis scales, and stylistic conventions specific to Russian government publications.

## Licenses

This dataset uses **mixed licensing**:

- **Code** (`eval/normalize.py`, `eval/eval_example.py`): Apache 2.0
- **Synthetic QA + images** (`synthetic/`): CC-BY 4.0 — author's original work
- **ChartReal QA annotations** (`chartreal/data.jsonl`): CC-BY 4.0 — author's original annotations
- **ChartReal images** (`chartreal/images/`): **research use only, original copyright preserved**. These are derivative works (PNG renderings of pages from public-domain government PDFs). Original publishers (Rosstat, Bank of Russia) retain copyright on the visual material. Re-use beyond academic research may require permission from the original publishers.

By using the `chartreal/images/` portion, you agree to:
1. Use it only for academic / non-commercial research
2. Cite both this dataset and the original publisher
3. Not redistribute the images independently of the QA annotations

## Citation

```bibtex
@dataset{ruchartqa_2026,
  title  = {RuChartQA: A Russian-Language Chart Question Answering Benchmark with Synthetic and Real-World Splits},
  author = {Roman <last name>},
  year   = {2026},
  url    = {https://huggingface.co/datasets/romath/RuChartQA},
  note   = {HSE Bachelor's thesis}
}
```

## Limitations

- **ChartReal is image-only.** A `text_description` variant for real-world charts is not provided — automatic transcription of complex line/mixed charts to faithful text without losing information turned out to be infeasible in practice.
- **Bar-bias in synthetic.** All synthetic charts are bar-type. Comparison fairness across chart types should use the bar-only subset of ChartReal (n=67) — see `results/leaderboard.csv`.
- **Answer normalizer judgement calls.** A small number of answers (≤2pp of total) are influenced by language-drift conventions: yes/no in English vs Russian, Roman vs Cyrillic month numerals. We chose conservative scoring (mismatch counted as wrong); reasonable alternatives exist.

## Contact

Questions, errata, or contributions: [your email or GitHub username].