Upload benchmark/README.md

benchmark/README.md

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+# 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
+
+```bash
+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:
+
+```bash
+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:
+
+```json
+{
+  "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:
+
+```json
+{
+  "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:
+
+```text
+outputs/
+  model-name/
+    N1/
+      results.jsonl
+    N2/
+      results.jsonl
+```
+
+Run:
+
+```bash
+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:
+
+```bash
+python scripts/compile_results.py \
+  --metrics-root metrics \
+  --judged-root outputs_judged \
+  --output-root compiled
+```
+
+Judge annotations may be nested under a `judge` key:
+
+```json
+{
+  "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
+
+```bash
+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`.