ArXiv

Item Response Theory for AI Safety

2026-08-05 · 17:25 UTC ·Joshua Fonseca Rivera, Neil Shah, David Demitri Africa... ·1 min read
Authors
Joshua Fonseca Rivera, Neil Shah, David Demitri Africa...
Categories
cs.AI, cs.CL
arXiv
https://arxiv.org/abs/2608.05086v1
PDF
https://arxiv.org/pdf/2608.05086v1

Brief

The paper applies Item Response Theory to LLM safety evaluation, fitting IRT models to eight benchmarks and 192 models (largest such study to date). It identifies three latent factors—refusal strictness, truthfulness, and contextual harm—as primary axes of variance, shows psychometrically chosen items (≈10 adaptive items) can recover scores while cutting costs 97–99%, and demonstrates IRT can detect sandbagging and API model changes, recommending adoption by evaluators.

Why it matters

Fitted Item Response Theory (IRT) to eight safety benchmarks across 192 language models (largest psychometric analysis of LLM safety evaluations to date) and found three interpretable latent factors—refusal strictness, truthfulness, and contextual harm—that explain most cross-model variance.

Key details

  • Psychometrically selected items recover full benchmark scores with lower error than random subsets; roughly ten adaptively chosen items suffice for several individual benchmarks, reducing evaluation cost by about 97–99%.
  • IRT enables model-level audits: it can detect naive sandbagging and changes to models behind APIs, and the authors recommend frontier labs and evaluators adopt IRT for reading, reducing, and auditing safety benchmarks.
Source evidence

Abstract

Language models differ in how safely they behave and these differences are measured by safety benchmarks. But aggregated benchmark scores are hard to trust and interpret, because benchmarks duplicate one another, correlate heavily, and models may sandbag when they detect evaluation. To address these issues, we draw on Item Response Theory (IRT), a statistical toolkit for measuring these latents from performance on items with inferred psychometric properties. We fit IRT models to eight safety benchmarks across 192 language models, the largest psychometric analysis of LLM safety evaluations to date, and contribute three results. First, we find that three interpretable factors of refusal strictness, truthfulness, and contextual harm explain most of the variance between models across benchmarks. Second, psychometrically selected items recover full benchmark scores with lower error than random subsets of the same size, and roughly ten adaptively chosen items suffice for several individual benchmarks, cutting evaluation cost by 97-99%. Third, IRT supports audits of individual models, showing that it can be used to detect naive sandbagging and changes of model behind APIs. Overall, we show IRT is a ready-made toolkit for reading, reducing, and auditing safety benchmarks, which we recommend frontier labs and evaluators adopt.

Comment: 15 pages, 9 figures, 6 tables