LLM-judge audits assess bias by comparing ratings across matched conditions. Difference-in-differences designs compare two candidate responses within each item and then compare that contrast across a manipulated attribute. We show in closed form that this endpoint need not identify differential preference on the latent scale when ratings are bounded. A severity shift common to both responses produces an observed interaction whenever the scale bounds attenuate it unequally. We examine this problem in a pre-registered audit comprising 990 calls to a frozen pedagogy judge. The registered primary analysis did not detect an effect of the stated learner profile on scaffolding preference. The only nominally significant secondary interaction concerned productive struggle ($+0.378$ points; $p = 0.002$). A post-hoc construction with zero differential preference reproduced 79 to 85\% of this interaction using the observed severity shift and the scale floor alone. The observed interaction therefore does not identify differential preference on the latent scale.
Bias audits of large language models (LLMs) typically use a single prompt to compare
model responses to identity-matched stimuli. This design rests on the implicit assumption
that the estimated identity gap is stable across plausible prompt wordings. We test this
assumption in a factorial experiment on three contempora...
Jia-He Sui, Xiang-Zhi Li· Advances in Artificial Intel...· 0 citations
This work treats LLM judges as raters and runs a pre-registered rater-effects battery on public corpora in two languages, finding no credible evidence that judge halo exceeds the trained-human range.
First-year computer-science students judged their grade on two assessments differing in judgment type: a concurrent self-assessment embedded in an early quiz (Assessment 1, N=101) and a postdiction after a mid-course written-and-laboratory exam (Assessment 2, N=117), with 94 students linked across both. The concurrent...
Géza Vekov, M. Csernoch· Education sciences· 0 citations
This study examined the mode comparability of paper-based (YDS) and computer-based (e-YDS) forms of a high-stakes foreign language examination administered in Türkiye. Rather than treating the analysis as a routine test equating, we framed it as a test of whether a change in administration mode leaves the measured cons...
Kübra Atalay Kabasakal, Tuba Gündüz· Frontiers in Psychology· 0 citations
Who the judge is can affect an LLM-as-judge result, but measuring that effect without confusing it with candidate quality is difficult. We study four open-weight families (Llama 3.1, Qwen 2.5, Gemma 2, and Yi 1.5) in a fully crossed pairwise design with 9,312 judgments. A common per-family statistic is strongly confoun...
David Ababio Awuni, Luke Achenie, Benjamin Tei Partey et al.· 1 citation
Aim: This study investigates how a deterministic pre-judgment rule, which assigns a verdict before LLM judges evaluate a response, affects estimates of inter-rater reliability, with particular attention to Cohen’s κ.
Methods: Using 600 paired judgments from the LoCoMo benchmark, we compared Cohen’s κ between two LLM j...
Akshaya Kumar· American Journal of Technolo...· 0 citations