Machine individuality: Separating genuine idiosyncrasy from response bias in large language models
As large language models (LLMs) are increasingly integrated into daily life, in roles ranging from high-stakes decision support to companionship, understanding their behavioral dispositions becomes critical. A growing literature uses psychometric inventories and cognitive paradigms to profile LLM dispositions. However, these approaches cannot determine whether behavioral differences reflect stable, stimulus-specific individuality or global response biases and stochastic noise. Here, we apply crossed random-effects models -- widely used in psychometrics to separate systematic effects -- to 74.9 million ratings provided by 10 open-weight LLMs for over 100,000 words across 14 psycholinguisti...
| Authors | Valentin Kriegmair, Dirk U. Wulff |
|---|---|
| Primary category | cs.AI |
| Published | 18 April 2026 |
| Last updated at source | 21 April 2026 |
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