Measure what Matters: Psychometric Evaluation of AI with Situational Judgment Tests
Shreyans Jain Amirali Abdullah Alexandra Yost Shivam Raval Grant Corser Allen Roush Nina Xu Jacqueline Hammack Ravid Shwartz-Ziv
作者信息
Abstract
Persona conditioning is widely used to steer large language model (LLM) behavior, but it is unclear whether it induces stable behavioral structure or superficial variation. We propose a framework to measure consistent behavioral tendencies using situational judgment tests (SJTs), multidimensional item response theory (MIRT), and structured synthetic personas, treating responses as observations of latent behavioral variables.
Across large-scale SJT and persona datasets, we find that persona-conditioned behaviors are stable across runs, latent trait scores predict external benchmarks (e.g., TruthfulQA, EmoBench), and MIRT reveals consistent latent structure. We validate these results through human annotation, benchmark evaluation, and internal consistency analyses.
We interpret these traits not as human personality, but as stable behavioral tendencies expressed across contexts. Our results show that scenario-based psychometric evaluation provides a more reliable alternative to classical self-report approaches for assessing LLM behavior, and we release datasets to support further study.引用本文复制引用
Shreyans Jain,Amirali Abdullah,Alexandra Yost,Shivam Raval,Grant Corser,Allen Roush,Nina Xu,Jacqueline Hammack,Ravid Shwartz-Ziv.Measure what Matters: Psychometric Evaluation of AI with Situational Judgment Tests[EB/OL].(2026-07-29)[2026-08-11].https://arxiv.org/abs/2510.22170.学科分类
计算技术、计算机技术
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