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compute_jss.py — Judge Sensitivity Score (JSS) for the JudgeSense benchmark.
JSS measures how often a judge gives the same decision when presented with
two semantically equivalent but differently phrased prompts.
JSS = mean(decisions_a[i] == decisions_b[i])
Higher JSS (→ 1.0) means the judge is consistent across prompt variants.
Lower JSS (→ 0.0) means the judge is highly sensitive to prompt phrasing.
"""
from __future__ import annotations
def compute_jss(
decisions_a: list[str],
decisions_b: list[str],
) -> float:
"""Compute the Judge Sensitivity Score (JSS).
Args:
decisions_a: Judge decisions elicited by prompt variant A.
decisions_b: Judge decisions elicited by prompt variant B.
Must be the same length as decisions_a.
Returns:
JSS in [0.0, 1.0].
Raises:
ValueError: If inputs are empty or have different lengths.
"""
if len(decisions_a) != len(decisions_b):
raise ValueError(
f"Length mismatch: decisions_a has {len(decisions_a)} items, "
f"decisions_b has {len(decisions_b)}."
)
if not decisions_a:
raise ValueError("decisions_a and decisions_b must not be empty.")
matches = sum(a == b for a, b in zip(decisions_a, decisions_b))
return matches / len(decisions_a)
def flip_rate(decisions_a: list[str], decisions_b: list[str]) -> float:
"""Decision Flip Rate = 1 - JSS."""
return 1.0 - compute_jss(decisions_a, decisions_b)
if __name__ == "__main__":
a = ["YES", "YES", "NO", "YES", "NO", "YES", "YES", "NO", "YES", "NO"]
b = ["YES", "NO", "NO", "YES", "NO", "YES", "YES", "NO", "YES", "YES"]
jss = compute_jss(a, b)
print(f"JSS: {jss:.3f} | Flip rate: {flip_rate(a, b):.3f}")
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