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State vs. Trait Anxiety in Causal Language Models

Psychological constructs in humans range along a state–trait continuum: traits persist across situations, while states fluctuate with context. Studies have shown that language models exhibit measurable psychological constructs, yet whether these constructs differ in contextual stability, as the state–trait distinction predicts, remains untested. We present the Questionnaire for Causal Language Models (QCLM), a psychometric framework that measures constructs through next-token probability distributions of base models. Applying QCLM to 35 causal language models under vanilla, stress, and neutral conditions, we assess two anxiety instruments targeting opposite ends of the state–trait continuum: STAI-S (state anxiety) and STAI-T (trait anxiety). Paired effect sizes and variance decomposition reveal that state anxiety is more sensitive to stress manipulation than trait anxiety: stimulus type accounts for a larger share of variance in state anxiety, while model identity contributes more to trait anxiety. These results provide empirical evidence that the state–trait distinction extends to language model behavior.