An LLM-native five-factor psychometric instrument shows self-reports fail to predict behavior even on constructs derived from LLM behavior, and LLM judges share a variance source humans do not.
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5 Pith papers cite this work, alongside 77 external citations. Polarity classification is still indexing.
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Apparent psychological profiles of LLMs are largely measurement artifacts driven by directional response bias rather than actual traits.
The primary axis of psychometric variation among LLMs is the degree to which they represent themselves as loci of phenomenal experience rather than systems of behavioral responses.
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.
STAMP-LLM is a two-phase psychometric protocol for designing and applying bias measures to LLMs, illustrated with one explicit and two implicit racial bias tests.
citing papers explorer
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An LLM-Native Psychometric Instrument Reveals a Self-Report--Behavior Gap Across 25 Models
An LLM-native five-factor psychometric instrument shows self-reports fail to predict behavior even on constructs derived from LLM behavior, and LLM judges share a variance source humans do not.
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Apparent Psychological Profiles of Large Language Models are Largely a Measurement Artifact
Apparent psychological profiles of LLMs are largely measurement artifacts driven by directional response bias rather than actual traits.
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The Pinocchio Dimension: Phenomenality of Experience as the Primary Axis of LLM Psychometric Differences
The primary axis of psychometric variation among LLMs is the degree to which they represent themselves as loci of phenomenal experience rather than systems of behavioral responses.
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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.
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Designing Psychometric Bias Measures for ChatBots: An Application to Racial Bias Measurement
STAMP-LLM is a two-phase psychometric protocol for designing and applying bias measures to LLMs, illustrated with one explicit and two implicit racial bias tests.