File size: 2,488 Bytes
2e511b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 | #!/usr/bin/env python3
"""Aggregate the published hallucination review into the Figure 3 numbers.
Reads ``data/analysis/hallucinations/hallucination_review_ch.csv`` (one row
per flagged (case, field, model) cell over the 30 Swiss judgments with the
highest flagged-cell counts; see ``scripts/export_hallucination_review.py``)
and prints the per-category shares and the two derived brackets shown in the
paper's hallucination figure.
Usage: uv run python scripts/hallucination_stats.py
"""
import argparse
import csv
from collections import Counter
from pathlib import Path
DEFAULT_CSV = Path("data/analysis/hallucinations/hallucination_review_ch.csv")
CATEGORY_LABELS = {
"A": "Fabrication (genuine hallucination)",
"B": "Misattribution (genuine hallucination)",
"C": "Gold-set gap (value present in judgment)",
"D": "Defensible coding (value present in judgment)",
"E": "Refusal",
}
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(description=__doc__.split("\n\n")[0])
parser.add_argument("--csv", type=Path, default=DEFAULT_CSV)
parser.add_argument("--per-model", action="store_true",
help="additionally break the categories down per system")
args = parser.parse_args(argv)
with args.csv.open(newline="", encoding="utf-8") as f:
rows = list(csv.DictReader(f))
n = len(rows)
counts = Counter(r["category"] for r in rows)
cases = {r["case_id"] for r in rows}
print(f"n = {n} flagged (case, field, model) cells over {len(cases)} judgments")
for cat in sorted(CATEGORY_LABELS):
c = counts.get(cat, 0)
print(f" {cat} {CATEGORY_LABELS[cat]:<45} {c:>3} {100 * c / n:.1f}%")
ab = counts.get("A", 0) + counts.get("B", 0)
cd = counts.get("C", 0) + counts.get("D", 0)
print(f" A+B Genuine hallucination {ab:>3} {100 * ab / n:.1f}%")
print(f" C+D Value present in judgment / gold-set gap {cd:>3} {100 * cd / n:.1f}%")
if args.per_model:
by_model: dict[str, Counter] = {}
for r in rows:
by_model.setdefault(r["model"], Counter())[r["category"]] += 1
print()
for model, c in sorted(by_model.items()):
total = sum(c.values())
cats = " ".join(f"{k}:{c.get(k, 0)}" for k in sorted(CATEGORY_LABELS))
print(f" {model:<30} n={total:<4} {cats}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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