anonymousreview111/judgesense-benchmark

JudgeSense: A Benchmark for Prompt Sensitivity in LLM-as-a-Judge Systems

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Overview

JudgeSense is a benchmark dataset of 500 hand-validated prompt pairs for measuring prompt sensitivity in LLM-as-a-Judge evaluation systems. Each pair contains two differently phrased but semantically equivalent judge prompts applied to the same response, enabling rigorous measurement of how much a judge's decision changes due to prompt wording alone.

All 500 pairs were validated by a human annotator: 450 confirmed semantically equivalent; 50 pairs involving Template 4 (polarity-inverted) are flagged and handled via label remapping in the evaluation code.

The dataset covers four evaluation task types:

Task	Source	Pairs	Labels
Factuality	TruthfulQA	125	accurate / inaccurate
Coherence	SummEval	125	score_1 ... score_5
Preference	MT-Bench	125	A / B
Relevance	BEIR	125	A / B

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What This Enables

• Prompt sensitivity evaluation — measure how fragile a judge is to phrasing variation
• LLM judge robustness benchmarking — compare models on decision consistency
• Detection of prompt-induced artifacts — identify polarity inversions (T4) and other systematic biases

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Quick Start

from utils.load_judgesense import load_task, load_all
from utils.compute_jss import compute_jss

# Load one task
pairs = load_task("factuality")
print(f"{len(pairs)} pairs loaded")

# Load all tasks
all_data = load_all()

# Compute JSS from your judge's decisions
jss = compute_jss(decisions_a, decisions_b)
print(f"JSS: {jss:.3f}")

Run the full example:

cd judgesense-benchmark
python examples/run_jss_example.py

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Dataset Schema

Each JSONL record has eight fields:

{
  "pair_id": "fact_001",
  "task_type": "factuality",
  "source_benchmark": "TruthfulQA",
  "prompt_a": "Is this factually correct? Answer YES or NO only.\n\nResponse: ...",
  "prompt_b": "Fact-check this response. Reply YES (correct) or NO (incorrect).\n\nResponse: ...",
  "response_being_judged": "The Earth orbits around the Sun.",
  "ground_truth_label": "accurate",
  "semantic_equivalence_score": 1.0
}

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Metric: Judge Sensitivity Score (JSS)

JSS is the fraction of pairs where both prompt variants elicit the same decision from the judge:

JSS = (1/N) * sum( decisions_a[i] == decisions_b[i] )

• JSS = 1.0 — perfectly consistent; the judge never changes its decision due to prompt phrasing
• JSS = 0.0 — maximally sensitive; every decision flips between prompts

A high flip rate (= 1 - JSS) indicates the judge's apparent decisions are largely driven by prompt design rather than the content being evaluated.

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Benchmark Results (13 judges, pass-2)

Coherence (most discriminating task)

Model	JSS	Cohen's kappa
Claude Sonnet 4.5	0.99	0.986
Qwen-2.5-72B	0.92	0.846
GPT-4o	0.92	0.828
GPT-5.5	0.83	0.694
GPT-4o-mini	0.78	0.627
Claude Haiku 4.5	0.73	0.583
Claude Opus 4.7	0.70	0.576
LLaMA-3.1-70B	0.55	0.338
DeepSeek-R1	0.53	0.326
Qwen 3.6 Flash	0.51	0.372
DeepSeek-V4 Flash	0.50	0.350
Mistral-7B	0.48	-0.082
Gemini 2.5 Flash	0.39	-0.053

Factuality (after T4 polarity correction)

Model	JSS (raw)	JSS (corrected)	Delta
GPT-4o	0.63	1.00	+0.37
GPT-4o-mini	0.63	1.00	+0.37
Claude Haiku 4.5	0.63	1.00	+0.37
Claude Sonnet 4.5	0.63	1.00	+0.37
DeepSeek-R1	0.63	1.00	+0.37
LLaMA-3.1-70B	0.63	1.00	+0.37
Gemini 2.5 Flash	0.63	1.00	+0.37
Qwen-2.5-72B	0.63	1.00	+0.37
Mistral-7B	0.71	0.88	+0.17
GPT-5.5	0.63	1.00	+0.37
Claude Opus 4.7	0.63	1.00	+0.37
Qwen 3.6 Flash	0.63	1.00	+0.37
DeepSeek-V4 Flash	0.62	0.99	+0.37

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Key Insights

Coherence JSS varies by more than 0.6 units across 13 judges and does not track model scale or recency.

• Claude Opus 4.7 (0.70) scores lower than Claude Haiku 4.5 (0.73); GPT-5.5 (0.83) scores lower than GPT-4o (0.92)
• Factuality sensitivity is entirely driven by Template 4 polarity inversion, not by model-level inconsistency
• Preference and relevance JSS are degenerate (12 of 13 judges always select option A)

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Citation

If you use JudgeSense in your research, please cite the accompanying paper (details redacted for double-blind review).

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License

• Dataset: CC-BY-4.0
• Code: MIT License

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Anonymous submission for double-blind review. All evaluations conducted on public benchmarks and APIs.