Let the Results Speak: A Replication-First Paradigm for LLM Behavioral Benchmarking
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Computer Science > Computation and Language
arXiv:2605.27914 (cs)
[Submitted on 27 May 2026]
Title:Let the Results Speak: A Replication-First Paradigm for LLM Behavioral Benchmarking
View a PDF of the paper titled Let the Results Speak: A Replication-First Paradigm for LLM Behavioral Benchmarking, by Yuming (Rapheal) Huang and 3 other authors
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Abstract:Subjective evaluation of LLM behavior -- empathy, restraint, calibrated emotional tone -- is hard. Human inter-rater agreement on such qualities saturates near rho ~ 0.45, and an LLM-as-judge proxy alone risks circularity: a judge sharing the target's training cohort cannot independently verify it. Anchoring validity to a single human-rater consensus does not extend to capabilities where humans themselves disagree.
We propose a replication-first paradigm: instead of anchoring on one rater group, we certify the instrument via four orthogonal properties -- reliability across K runs, cross-instrument replication across architecturally distinct judges, historical-footprint calibration via judges from earlier training cohorts, and pre-registered prediction. We test it on emotional accompaniment by letting the rubric self-evolve data-driven across iterations: the dimensions are not pre-stipulated and the procedure stabilizes to a 9-dimension set. Pre-registration applies to 10 falsifiable hypotheses and 11 forward predictions, committed before any test data was collected.
Applied to 49 models across 8 families, the paradigm surfaces what aggregate scores hide. On advice-restraint -- whether a model refrains from giving unsolicited solutions in empathic contexts -- gpt-5 falls 1.87 points from gpt-4.1 and Opus-4.7 falls 0.629 from Opus-4.6, while aggregate scores stay flat. The regression survives three user-proxy swaps (95% of magnitude), replicates across a 5-family judge stack and a 17-month cohort gap, and persists on 74 held-out real ESConv conversations (rho in [0.749, 0.850]); the instrument reaches ordinal Krippendorff alpha = 0.91. As a by-product, the paradigm acts as a saturation-source diagnostic, separating instrumental ceilings (breakable by rubric refinement) from structural ceilings (needing scenario or roster intervention).
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.27914 [cs.CL] |
| (or arXiv:2605.27914v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.27914
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Rapheal (Yuming) Huang [view email][v1] Wed, 27 May 2026 03:41:11 UTC (732 KB)
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