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Datasets:
alialp207
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TR-DataAnalystBench

Tasks:
Question Answering
Table Question Answering
Modalities:
Image
Tabular
Text
Formats:
json
Languages:
Turkish
Size:
1K - 10K
Tags:
turkish
data-analysis
tables
charts
reasoning
benchmark
Libraries:
Datasets
pandas
Polars
License:
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metadata 
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:
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 

@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}}
}