Resolution Diagnostics for Paired LLM Evaluation
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Abstract (arXiv)
Across two public LLM leaderboards, many displayed pairwise rankings do not meet a conventional paired-test resolution target under the actual paired evaluation design: 11 of 40 Open LLM Leaderboard v1 pairwise comparisons and 4 of 9 MMLU-Pro top-10 adjacent-rank pairs are unresolved at (alpha, 1-beta) = (0.05, 0.8). The MMLU-Pro count rises to 6/9 under real subject-level clustering and stays at 5-6 out of 9 in 99.9% of category-bootstrap resamples. We frame paired LLM evaluation as a hypothesis-testing problem, invert level-alpha, power-(1-beta) tests, and report a per-pair resolution ratio q = N/N* as the primary diagnostic. A sharp small-effect expansion with an explicit second-order constant shows that the widely-used unpaired Cohen-h-plus-(1-rho) shortcut deviates from the correct N* by approximately a factor of two in the close-comparison regime, a deficit that three of five off-the-shelf calculators(Cohen 1988, G*Power, R pwr) silently inherit when the user post-multiplies their per-arm output by (1-rho). The unresolved-pair pattern remains under multiplicity correction and anytime-valid sequential testing.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 10 of its 25 references that are in the map. First posted on arXiv 28 May 2026.
Builds on
10 of 25 references.
References · 10
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Citing papers per year: 2026: 7.
7 papers.
- 2026Pay Only for Disagreement: Certified No-Regression Verdicts for Model Updates with Matching Label-Complexity Boundscited by 2
- 2026Rank Confidence Sequences:Anytime-valid Leaderboardscited by 2
- 2026Are Near-Tied LLM Rankings Robust to Family-DIF-Guided Benchmark Recomposition?cited by 1
- 2026Discovering Translation-Worthy Languages with E-Valuescited by 0
- 2026Coding Agents Have Converged: Why the SWE-bench Leaderboard Can No Longer Order Its Top Entries, and What to Measure Insteadcited by 0
- 2026How Sensitive Are LLM Leaderboard Claims to Hidden Model Selection?cited by 0
- 2026Are We Testing Too Much? A Survey of Benchmark Compression for Efficient Evaluation of Large Language Modelscited by 0
- 2026Are We Testing Too Much? A Survey of Benchmark Compression for Efficient Evaluation of Large Language Models13 Oct 2026
- 2026Rank Confidence Sequences:Anytime-valid Leaderboards26 Sep 2026
- 2026How Sensitive Are LLM Leaderboard Claims to Hidden Model Selection?23 Sep 2026
- 2026Coding Agents Have Converged: Why the SWE-bench Leaderboard Can No Longer Order Its Top Entries, and What to Measure Instead15 Sep 2026
- 2026Discovering Translation-Worthy Languages with E-Values6 Sep 2026
- 2026Are Near-Tied LLM Rankings Robust to Family-DIF-Guided Benchmark Recomposition?31 Aug 2026
- 2026Pay Only for Disagreement: Certified No-Regression Verdicts for Model Updates with Matching Label-Complexity Bounds22 Jul 2026