arXiv: cs.AI preprintsInternational6 November 2026
The Unsampled Truth: Quantifying Prompt Artifacts in LM Psychometrics
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arXiv:2606.03357v3 Announce Type: replace-cross Abstract: When prompting language models for psychometric assessment, researchers assume that the responses reflect the injected persona and the meaning of the survey item. We test this premise using a diagnostic design that crosses five semantically distinct baseline personas with five semantically equivalent variants of each of four prompt components (persona wording, task instruction, item wording, option symbol). Measuring the 1-Wasserstein distance between the resulting response distributions and partitioning the variation among the five com
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