| --- |
| license: cc-by-4.0 |
| language: |
| - tr |
| pretty_name: TR-DataAnalystBench |
| size_categories: |
| - n<1K |
| task_categories: |
| - question-answering |
| - table-question-answering |
| tags: |
| - turkish |
| - data-analysis |
| - tables |
| - charts |
| - reasoning |
| - benchmark |
| - evaluation |
| configs: |
| - config_name: synthetic_v01 |
| data_files: |
| - split: train |
| path: data/synthetic_v01/train.jsonl |
| - split: validation |
| path: data/synthetic_v01/validation.jsonl |
| - split: test |
| path: data/synthetic_v01/test.jsonl |
| - config_name: synthetic_v02 |
| data_files: |
| - split: train |
| path: data/synthetic_v02/train.jsonl |
| - split: validation |
| path: data/synthetic_v02/validation.jsonl |
| - split: test |
| path: data/synthetic_v02/test.jsonl |
| - config_name: real_pilot |
| data_files: |
| - split: train |
| path: data/real_pilot/train.jsonl |
| - split: validation |
| path: data/real_pilot/validation.jsonl |
| - split: test |
| path: data/real_pilot/test.jsonl |
| - config_name: chart_read_v01 |
| data_files: |
| - split: train |
| path: data/chart_read_v01/train.jsonl |
| - split: validation |
| path: data/chart_read_v01/validation.jsonl |
| - split: test |
| path: data/chart_read_v01/test.jsonl |
| - config_name: real_anon_v01 |
| data_files: |
| - split: train |
| path: data/real_anon_v01/train.jsonl |
| - split: validation |
| path: data/real_anon_v01/validation.jsonl |
| - split: test |
| path: data/real_anon_v01/test.jsonl |
| - config_name: reasoning_v01 |
| data_files: |
| - split: train |
| path: data/reasoning_v01/train.jsonl |
| - split: validation |
| path: data/reasoning_v01/validation.jsonl |
| - split: test |
| path: data/reasoning_v01/test.jsonl |
| - config_name: chart_hard_v01 |
| data_files: |
| - split: train |
| path: data/chart_hard_v01/train.jsonl |
| - split: validation |
| path: data/chart_hard_v01/validation.jsonl |
| - split: test |
| path: data/chart_hard_v01/test.jsonl |
| --- |
| |
| # TR-DataAnalystBench |
|
|
| A Turkish-language benchmark for evaluating whether language models can perform |
| **data-analyst style reasoning over tables and charts**: reading a value, |
| finding the maximum/minimum, comparing two years, computing an average or a |
| (signed) percentage change, ranking, summarizing a trend, and — importantly — |
| **abstaining when the data does not contain the answer**. |
|
|
| Gold answers are computed and verified with Python (not produced by a language |
| model), so the benchmark is reproducible and auditable. An automatic evaluator |
| scores numeric (tolerance), categorical (trend), and abstention tasks. |
|
|
| ## Why this benchmark |
|
|
| Many models are fluent in Turkish yet still fail at numerical reasoning, table |
| understanding, and chart interpretation. TR-DataAnalystBench isolates those |
| abilities with verifiable gold answers and a transparent scoring contract. |
|
|
| ## The suite (1,436 examples, seven tiers) |
|
|
| | Tier | Examples | Tasks | What it targets | |
| |---|---:|---:|---| |
| | `synthetic_v01` | 300 | 5 | Easy/medium baseline: single-series tables, basic lookups/compare/percentage | |
| | `synthetic_v02` | 320 | 8 | Harder & discriminative: multi-series tables, distractor columns, average / nth-highest / cross-series, unanswerable questions, real `hard` labels | |
| | `real_pilot` | 108 | 7 | **Real Türkiye open data** (population, GDP, consumer inflation, CO₂) with verified gold | |
| | `chart_read_v01` | 240 | 5 | **Genuine chart reading**: label-free charts (no printed values); read which year is max/min, compare years, count above a level, estimate a value, read the trend | |
| | `real_anon_v01` | 108 | 7 | **Contamination-controlled real data**: real series with the country/years removed and per-series rescaling, so it measures table reading rather than recall | |
| | `reasoning_v01` | 180 | 6 | **Hard multi-step reasoning**: CAGR, fastest-growth year, longest increase streak, conditional average, share of total, ratio between two series | |
| | `chart_hard_v01` | 180 | 6 | **Discriminative chart reading**: cluttered two-series, 12-year, off-gridline label-free charts; tight ±5% value reading, closest-pair comparisons, cross-series scanning — designed to challenge frontier vision models | |
|
|
| Splits are table-disjoint (the questions sharing a table/chart never cross a |
| split boundary). |
|
|
| `chart_read_v01` is the only tier whose charts carry **no data labels**, so it |
| measures reading values off the axes/gridlines rather than label OCR. Its |
| exact-scored tasks (which year? / compare / count / trend) need only the chart's |
| shape, while `value_estimate` is scored with an ±8% estimation tolerance. |
|
|
| ## Task types |
|
|
| | Task | Answer | Scoring | |
| |---|---|---| |
| | `value_lookup` | a value | numeric, ±2% tolerance | |
| | `max_min` / `nth_highest` | an extreme / ranked value | numeric | |
| | `comparison` | absolute difference between two years | numeric | |
| | `cross_series_diff` | difference between two series in a year | numeric | |
| | `average` | mean of a series | numeric | |
| | `percentage_change` | **signed** percent change | numeric, ±2 percentage points | |
| | `trend_summary` | `artış` / `azalış` / `dalgalı` | categorical label match | |
| | `unanswerable` | abstention (`veri yok`) | correct iff the model declines | |
|
|
| Input formats: `table_only`, `chart_only` (chart image, no table — prevents |
| table leakage), and `table_and_chart`. |
|
|
| ## Data fields |
|
|
| Each example is a JSON object with, among others: |
|
|
| - `id`, `dataset_version`, `language` (`tr`), `domain`, `split` |
| - `question_type`, `difficulty`, `input_format`, `chart_type`, `chart_path` |
| - `table`: `{ "columns": [...], "rows": [[...], ...] }` |
| - `question`, `answer` (human-readable gold) |
| - `answer_type`: `numeric` | `numeric_with_label` | `text` (trend) | `abstention` |
| - `numeric_answer` (or `null`), `trend_class` (for trends), `target_column`, `unit` |
| - `calculation` (how the gold was derived) |
| - `real_pilot` only: `source_name`, `source_url`, `license`, `country` |
|
|
| ## How to evaluate a model |
|
|
| 1. Build prompts from the dataset (a prompt for each example; for `chart_only` |
| the model is given the chart image, not the table). |
| 2. Collect answers into a CSV with columns `id`, `predicted_numeric_answer`, |
| and `prediction_text` (used for trend words and `veri yok`). |
| 3. Score with the repository's evaluator: |
|
|
| ```bash |
| python scripts/08_evaluate_predictions_file.py \ |
| --dataset data/processed/real_pilot.jsonl \ |
| --predictions your_predictions.csv --split test |
| ``` |
|
|
| The evaluator reports overall accuracy plus per-kind accuracy (numeric |
| tolerance, trend label, abstention) broken down by task, input format, and |
| domain. Running it on the provided oracle predictions yields 100%, confirming |
| the scoring pipeline. |
|
|
| ## Baselines |
|
|
| | System | Tier | Accuracy | Notes | |
| |---|---|---:|---| |
| | Oracle (gold) | all | 100% | scoring sanity check (all six tiers) | |
| | Noisy baseline | synthetic_v01 | ~72% | programmatic perturbation reference | |
| | Noisy baseline | synthetic_v02 | ~66% | abstention ~45% (catches hallucination) | |
| | "Simple-%" error | reasoning_v01 | ~83% | a model that confuses CAGR with simple % change loses exactly the CAGR items | |
| | ChatGPT (manual, 12-item sample) | real_pilot | ~92% | by-hand run; perfect on numeric & abstention, missed one borderline trend | |
| | ChatGPT (manual, bulk, 18-item) | reasoning_v01 test | ~100% | every well-formed item correct (incl. CAGR); even flagged a malformed question, which led to a gold fix — frontier models are strong at numeric reasoning | |
| | GPT-4o / Gemini / Claude (manual, 16-item) | chart_read probe | 100% / 100% / 100% | all three tied — simple, round-valued label-free charts are not discriminative; this finding motivated `chart_hard_v01` (cluttered, off-gridline) | |
| | GPT-4o / Claude / Gemini (manual, 18-item) | chart_hard_v01 test | 100% / 100% / 83% | first separation between frontier models — Gemini missed a 2nd-highest, a count, and a cross-series "closest year" item | |
| | GPT-4o / Claude / Gemini (manual, 51-item) | chart_hard probe | **98% / 90% / 86%** | the dense hard-chart probe **separates all three frontier models**; the gaps come from careful-scanning tasks (cross-series closest/furthest year, counts, min-year, closest comparison), not from reading round values | |
| |
| The ChatGPT number is a small, manually collected illustration, not a full |
| leaderboard entry. The repository includes a free **manual evaluation kit** |
| (`scripts/16_create_manual_kit.py`) so anyone can reproduce/extend it without |
| an API. |
|
|
| ## Limitations |
|
|
| - In the synthetic/real tiers, charts carry printed data labels, so their |
| `chart_only` items partly measure label OCR. The `chart_read_v01` tier |
| removes labels to isolate genuine chart reading; expand it to make visual |
| reading a larger share of the suite. |
| - `real_pilot` uses real, well-known figures, so it can partly reward recall |
| rather than table reading; the `real_anon_v01` tier controls for this by |
| removing the country/years and rescaling each series. Both are kept so users |
| can compare authentic-value vs contamination-controlled settings. |
| - With a few hundred examples, overall rankings are stable but fine-grained |
| per-subgroup numbers carry meaningful confidence intervals. |
| - Trends are labeled by a deterministic rule (monotonic, or net change ≥5% with |
| a dominant direction, else `dalgalı`); some borderline series are debatable. |
|
|
| ## Licensing and provenance |
|
|
| - Datasets: **CC-BY-4.0**. `real_pilot` is derived from World Bank Open Data and |
| CDIAC emissions data (ODC-PDDL-1.0 / CC-BY-4.0); per-source provenance and |
| licenses are in `data/sources_real/provenance.json`. Synthetic tiers are |
| original work. |
| - Code: MIT (see `LICENSE`). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{harac2026trdataanalystbench, |
| title = {TR-DataAnalystBench: A Turkish Table and Chart Reasoning Benchmark}, |
| author = {Hara\c{c}, Ali Alp}, |
| year = {2026}, |
| howpublished = {\url{https://github.com/alialp5959/TR-DataAnalystBench}} |
| } |
| ``` |
|
|
