docs/chartbench_e_rebuilt_README.md

ChartBench-E (Rebuilt, Insight-Oriented)

This implementation rebuilds ChartBench-E and extends it into a more diagnostic benchmark for VLM chart reading.

What it provides

• 20 data patterns grouped into 6 behavior families.
• Multiple chart morphologies: line, bar_v, bar_h, stacked.
• Annotation modes: labeled, partial, unlabeled.
• Multi-factor difficulty beyond point count:
  • visual perturbations (noise, blur, JPEG artifacts, partial occlusion)
  • semantic traps (truncated axis, log-scale reading, legend confusion)
  • event-level stressors (outlier spikes, near-tie values, regime shifts)
• Diagnostic metadata tags for capability slicing:
  • event_tags
  • perceptual_tags
  • semantic_risk_tags
• Reproducible generation with random seed.
• Evaluation metrics:
  • SPR (Success Parsing Rate)
  • OA (Overall Accuracy)
  • NRA (Numerical Reading Accuracy)
  • ARE (Average Relative Error)

Install

cd chartbench_e
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Build benchmark-focused dataset

PYTHONPATH=. python scripts/build_chartbench_e.py \
  --out output/chartbench_e_benchmark_plus \
  --total-samples 20000 \
  --seed 42 \
  --task-mode benchmark \
  --challenge-level standard

Challenge levels:

• standard: light perturbation, mostly clean charts.
• advanced: balanced stress setting.
• extreme: heavy perturbation and denser hard cases.

Make fixed evaluation subset

PYTHONPATH=. python scripts/make_eval_subset.py \
  --index output/chartbench_e_benchmark_plus/metadata/index.jsonl \
  --out output/chartbench_e_benchmark_plus/metadata/eval_subset.jsonl \
  --per-task 400

Risk-focused subset example:

PYTHONPATH=. python scripts/make_eval_subset.py \
  --index output/chartbench_e_benchmark_plus/metadata/index.jsonl \
  --out output/chartbench_e_benchmark_plus/metadata/eval_compositional.jsonl \
  --per-task 200 \
  --require-risk-tag compositional_reasoning

Evaluate predictions

Prediction format (jsonl):

{"sample_id": "cb_0000001", "values": [[...], [...]]}

Run:

PYTHONPATH=. python scripts/evaluate_predictions.py \
  --truth output/chartbench_e_benchmark_plus/metadata/eval_subset.jsonl \
  --pred your_predictions.jsonl \
  --out output/report.json \
  --tolerance 0.05

Multi-model benchmark (new)

Supported model registry

See scripts/model_registry.json.

Default enabled models:

• gpt-5.2
• gpt-5.2-high
• gpt-5
• gpt-5-mini-high
• qwen3-vl-30b-a3b-thinking
• qwen2.5-vl-72b
• glm-4.6v

Explicitly excluded in this round:

• deepseek-vl2
• paddleocr-vl-1.5

Required env vars

OpenAI provider:

• OPENAI_API_KEY
• OPENAI_BASE_URL (optional)

SiliconFlow provider:

• SILICONFLOW_API_KEY
• SILICONFLOW_BASE_URL (optional, default https://api.siliconflow.cn/v1)

Fallback rule for SiliconFlow models:

• if SILICONFLOW_* is missing, script falls back to OPENAI_*

Prompt behavior

• The benchmark runner defaults to --prompt-mode type_specific.
• In type_specific mode, prompts are loaded from prompts/chart_types/<chart_type>.txt.
• Each prompt instantiation now includes a shape-aware JSON example through {json_example}, in addition to the strict JSON output contract.
• prompts/chart_reading_prompt.txt remains available for --prompt-mode unified.

Run benchmark

Single model + 3 repeats + evaluation:

PYTHONPATH=. python scripts/run_vlm_benchmark.py \
  --truth-index output/chartbench_e_benchmark_plus/metadata/eval_subset.jsonl \
  --dataset-root output/chartbench_e_benchmark_plus \
  --models gpt-5.2 \
  --repeats 3 \
  --max-workers 2 \
  --max-retries 2 \
  --timeout-seconds 120 \
  --run-eval \
  --tolerance 0.05

All enabled models:

PYTHONPATH=. python scripts/run_vlm_benchmark.py \
  --truth-index output/chartbench_e_benchmark_plus/metadata/eval_subset.jsonl \
  --dataset-root output/chartbench_e_benchmark_plus \
  --models all \
  --repeats 3 \
  --max-workers 4 \
  --max-retries 2 \
  --timeout-seconds 120 \
  --run-eval

Offline smoke test (no API call, sanity-only):

PYTHONPATH=. python scripts/run_vlm_benchmark.py \
  --truth-index output/chartbench_e_benchmark_plus/metadata/eval_subset.jsonl \
  --dataset-root output/chartbench_e_benchmark_plus \
  --models gpt-5.2 \
  --repeats 3 \
  --max-samples 20 \
  --run-eval \
  --mock-from-truth

Prompt A/B pilot

This pilot samples a 54-chart subset (18 chart types x 3 difficulty tiers x 1 sample), runs baseline vs. updated chart-type prompts, and writes a comparison report:

PYTHONPATH=. python scripts/run_prompt_ab_pilot.py \
  --mock-from-truth

For real model runs, set provider credentials first and rerun without --mock-from-truth.

Output structure

output/bench_runs/{run_id}/

• run_config.json
• truth_subset.jsonl
• run_summary.json
• leaderboard.csv
• models/{model_key}/repeat_0x/pred.jsonl
• models/{model_key}/repeat_0x/report.json
• models/{model_key}/repeat_0x/failures.jsonl
• models/{model_key}/repeat_0x/raw_responses.jsonl
• models/{model_key}/repeat_0x/slice_report.json
• models/{model_key}/model_stability.json
• models/{model_key}/sample_stability.jsonl
• models/{model_key}/slice_summary.json

Slicing coverage:

• difficulty_tier (L1-L4)
• theme_domain
• axis_scale
• y_axis_truncated

Output structure (dataset)

• images/*.png: generated chart images.
• metadata/index.jsonl: full metadata with ground truth and challenge tags.
• metadata/summary.json: split and distribution summary.
• metadata/eval_subset.jsonl: fixed benchmark subset.