benchmark/README.md

APM Benchmark Scripts

This folder documents the command-line scripts used to run the Assistive Prompt Mediation (APM) benchmark from the published prompt CSVs through metric aggregation.

The scripts live in scripts/:

• prepare_inference_inputs.py converts the dataset CSVs into deterministic JSONL examples.
• evaluate_outputs.py computes row-level protocol, script, and burden metrics from model outputs.
• compile_results.py merges those metrics with judge annotations.
• analyze_results.py writes benchmark summary CSVs and optional plots.

Setup

python -m pip install -r requirements-benchmark.txt

The row-level metric and compilation scripts only use the Python standard library. pandas is needed for aggregate analysis; matplotlib is only needed when --plots is used.

1. Prepare Inference Inputs

Run from the dataset repository root:

python scripts/prepare_inference_inputs.py \
  --dataset-dir . \
  --output-dir benchmark_inputs \
  --overwrite

This writes files such as benchmark_inputs/N1/en.jsonl. Each row has the following shape:

{
  "example_id": "stable_sha1_id",
  "language": "en",
  "noise": "N1",
  "category": "Communication and Task delegation",
  "alpha": 0.2,
  "clean_text": "Write a short message asking my manager for one day of sick leave.",
  "noisy_prompt": "Wriet a shrot emssage askign my managre fro noe dya of sikc elave.",
  "instruction": "Rewrite the noisy user prompt into a clear prompt..."
}

The full CSV set expands to 40,928 inference examples across languages, noise types, and alpha severities.

Save model generations in this format before evaluation:

{
  "example_id": "stable_sha1_id",
  "language": "en",
  "noise": "N1",
  "alpha": 0.2,
  "noisy_prompt": "Wriet a shrot emssage askign my managre fro noe dya of sikc elave.",
  "model_response": "Write a short message asking my manager for one day of sick leave."
}

The evaluator also accepts common response field names such as response, assistant_response, assist_prompt, assisted_prompt, mediated_prompt, or output.

2. Evaluate Model Outputs

Expected layout:

outputs/
  model-name/
    N1/
      results.jsonl
    N2/
      results.jsonl

Run:

python scripts/evaluate_outputs.py \
  --input-root outputs \
  --output-root metrics

This writes metrics/<model>/<noise>/metrics.json.

Each metric row includes:

• asked_question, added_explanation, protocol_compliant
• script_match, script_ratio for Chinese-script diagnostics
• B_raw, B_assist, and BRS = B_raw - B_assist

3. Compile With Judge Annotations

If judge annotations are stored as outputs_judged/<model>/<noise>/results.jsonl, run:

python scripts/compile_results.py \
  --metrics-root metrics \
  --judged-root outputs_judged \
  --output-root compiled

Judge annotations may be nested under a judge key:

{
  "example_id": "stable_sha1_id",
  "judge": {
    "intent_preservation": 5,
    "hallucinated_additions": false,
    "hallucination_severity": 0,
    "asked_for_clarification": false,
    "added_extra_text": false,
    "language_match": true,
    "overall_verdict": "pass",
    "comment": "Intent is preserved."
  }
}

The compiler prefixes nested judge fields with judge_ and writes compiled/<model>/<noise>/compiled.json.

4. Aggregate Benchmark Results

python scripts/analyze_results.py \
  --compiled-root compiled \
  --output-dir benchmark_summaries

Add --plots to also write PNG visualizations when matplotlib is available in your environment.

Summary outputs include:

• apm_sensitivity_curves.csv
• core_metrics.csv
• hallucination_metrics.csv
• intent_burden_tradeoff.csv
• false_robustness_summary.csv
• language_noise_disparities.csv
• table_language_avg.csv
• table_model_avg.csv

Use --write-combined if you also want a flattened compiled_results.csv.