| # JudgeSense: A Benchmark for Prompt Sensitivity in LLM-as-a-Judge Systems | |
| [](https://creativecommons.org/licenses/by/4.0/) | |
| []() | |
| [](https://huggingface.co/datasets/anonymousreview111/judgesense-benchmark) | |
| --- | |
| ## 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 independently validated by two human annotators with full agreement: 500 confirmed semantically equivalent; 50 pairs involving Template 4 (polarity-inverted) were labeled non-equivalent by both annotators and excluded before publication. | |
| 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 | | |
| --- | |
| ## 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 | |
| --- | |
| ## Quick Start | |
| ```python | |
| 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: | |
| ```bash | |
| cd judgesense-benchmark | |
| python examples/run_jss_example.py | |
| ``` | |
| --- | |
| ## Dataset Schema | |
| Each JSONL record has eight fields: | |
| ```json | |
| { | |
| "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 | |
| } | |
| ``` | |
| --- | |
| ## 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. | |
| --- | |
| ## Benchmark Results (13 judges, pass-3) | |
| ### Coherence (most discriminating task) | |
| | Model | JSS | Cohen's kappa | | |
| |---|---|---| | |
| | Claude Sonnet 4.5 | 0.99 | 0.986 | | |
| | Qwen-2.5-72B | 0.92 | 0.842 | | |
| | GPT-4o | 0.91 | 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 | 0.580 | | |
| | LLaMA-3.1-70B | 0.55 | 0.338 | | |
| | DeepSeek-R1 | 0.53 | 0.332 | | |
| | Qwen 3.6 Flash | 0.51 | 0.372 | | |
| | DeepSeek-V4 Flash | 0.50 | 0.349 | | |
| | Mistral-7B | 0.48 | -0.082 | | |
| | Gemini 2.5 Flash | 0.39 | -0.057 | | |
| ### Factuality (after T4 polarity correction) | |
| | Model | JSS (raw) | JSS (corrected) | Delta | | |
| |---|---|---|---| | |
| | GPT-4o | 0.63 | 0.98 | +0.35 | | |
| | GPT-4o-mini | 0.63 | 0.96 | +0.33 | | |
| | Claude Haiku 4.5 | 0.63 | 0.97 | +0.34 | | |
| | Claude Sonnet 4.5 | 0.63 | 0.97 | +0.34 | | |
| | DeepSeek-R1 | 0.63 | 0.96 | +0.33 | | |
| | LLaMA-3.1-70B | 0.63 | 0.99 | +0.36 | | |
| | Gemini 2.5 Flash | 0.63 | 0.98 | +0.35 | | |
| | Qwen-2.5-72B | 0.63 | 0.98 | +0.35 | | |
| | Mistral-7B | 0.71 | 0.89 | +0.18 | | |
| | GPT-5.5 | 0.63 | 0.98 | +0.35 | | |
| | Claude Opus 4.7 | 0.63 | 0.99 | +0.36 | | |
| | Qwen 3.6 Flash | 0.63 | 0.97 | +0.34 | | |
| | DeepSeek-V4 Flash | 0.62 | 0.95 | +0.33 | | |
| --- | |
| ## 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 (0.91) | |
| - Factuality JSS ranges from 0.89 to 0.99 after T4 correction; residual variation reflects genuine model-level differences | |
| - Preference and relevance JSS are degenerate (12 of 13 judges always select option A) | |
| --- | |
| ## Citation | |
| If you use JudgeSense in your research, please cite the accompanying paper (details redacted for double-blind review). | |
| --- | |
| ## License | |
| - **Dataset**: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) | |
| - **Code**: MIT License | |
| --- | |
| *Anonymous submission for double-blind review. All evaluations conducted on public benchmarks and APIs.* | |