Difference-in-Differences on a Censored Rating Scale Can Manufacture an Effect: Evidence from a Pre-Registered LLM-Judge Audit
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Abstract (arXiv)
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.
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References · 18
- 2024A Survey on LLM-as-a-Judgecited by 1,423
- 2023Large Language Models are not Fair Evaluatorscited by 1,085
- 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 686
- 2024Humans or LLMs as the Judge? A Study on Judgement Biasescited by 323
- 2024Quantifying the Persona Effect in LLM Simulationscited by 169
- 2023MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problemscited by 131
- 2023Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Promptingcited by 81
- 2022The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialoguescited by 79
- 2024Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutorscited by 61
- 2024Stepwise Verification and Remediation of Student Reasoning Errors with Large Language Model Tutorscited by 41
- 2021Preregistering NLP Researchcited by 25
- 2024LearnLM: Improving Gemini for Learningcited by 25
6 more
- 2026Grading Scale Impact on LLM-as-a-Judge: Human-LLM Alignment Is Highest on 0-5 Grading Scalecited by 20
- 2023Pre-registration for Predictive Modelingcited by 13
- 2025Prestige over merit: An adapted audit of LLM bias in peer reviewcited by 5
- 2026Intersectional Sycophancy: How Perceived User Demographics Shape False Validation in Large Language Modelscited by 2
- 2020Difference-in-Differences for Ordinal Outcomes: Application to the Effect of Mass Shootings on Attitudes toward Gun Controlcited by 1
- 2026Rethinking LLM-Judged Helpfulness as a Pedagogy Signal: A Pre-Registered Audit Across Tutor Modelscited by 1
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