The Unsampled Truth: Quantifying Prompt Artifacts in LM Psychometrics
Quick summary
arXiv:2606.03357v2 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
Key takeaways
- arXiv:2606.03357v2 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
Why it matters
“The Unsampled Truth: Quantifying Prompt Artifacts in LM Psychometrics” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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