Crossed random-effects models on LLM word ratings show 16.9% variance from genuine stimulus-specific individuality, exceeding null models and forming coherent per-model fingerprints.
4.Repetition cap.Stochastic repetitions were capped at 5 per (model, norm, word) tuple
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Machine individuality: Separating genuine idiosyncrasy from response bias in large language models
Crossed random-effects models on LLM word ratings show 16.9% variance from genuine stimulus-specific individuality, exceeding null models and forming coherent per-model fingerprints.