{"paper":{"title":"Measure what Matters: Psychometric Evaluation of AI with Situational Judgment Tests","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Situational judgment tests combined with multidimensional item response theory extract stable latent behavioral tendencies from persona-conditioned large language models.","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Alexandra Yost, Allen Roush, Amirali Abdullah, Grant Corser, Jacqueline Hammack, Nina Xu, Ravid Shwartz-Ziv, Shivam Raval, Shreyans Jain","submitted_at":"2025-10-25T05:45:10Z","abstract_excerpt":"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.\n  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., Tr"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Persona-conditioned behaviors are stable across runs, latent trait scores predict external benchmarks (e.g., TruthfulQA, EmoBench), and MIRT reveals consistent latent structure.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That responses to situational judgment tests can be treated as observations of stable latent behavioral variables in LLMs rather than prompt-dependent or superficial patterns.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Proposes SJTs and MIRT to measure consistent latent behavioral tendencies in LLMs, showing stability and predictive validity on external benchmarks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Situational judgment tests combined with multidimensional item response theory extract stable latent behavioral tendencies from persona-conditioned large language models.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4b6bb8530b9e8dca1bc4011f60b4f45b99f7986e5e5005bde33fc575561e62c4"},"source":{"id":"2510.22170","kind":"arxiv","version":3},"verdict":{"id":"4ee7c384-e35e-4319-b714-ec5e106ce582","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-18T04:49:24.475383Z","strongest_claim":"Persona-conditioned behaviors are stable across runs, latent trait scores predict external benchmarks (e.g., TruthfulQA, EmoBench), and MIRT reveals consistent latent structure.","one_line_summary":"Proposes SJTs and MIRT to measure consistent latent behavioral tendencies in LLMs, showing stability and predictive validity on external benchmarks.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That responses to situational judgment tests can be treated as observations of stable latent behavioral variables in LLMs rather than prompt-dependent or superficial patterns.","pith_extraction_headline":"Situational judgment tests combined with multidimensional item response theory extract stable latent behavioral tendencies from persona-conditioned large language models."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2510.22170/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"7100164c901550f5249dab3e8b9ca674d373c70582bf7081f61cfdb60b1d9a2c"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}