| --- |
| 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]. |
|
|
