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.
Dinić, and Ljubiša Bojić
3 Pith papers cite this work, alongside 19 external citations. Polarity classification is still indexing.
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Zero-shot LLM agents with human personas predict individual social media reactions better than chance (MCC 0.29) but worse than conventional text classifiers (MCC 0.36).
Standard psychometric questionnaires like the Big Five and PVQ produce different and more consistent results than ecologically valid questions drawn from real user conversations, suggesting the former may mischaracterize LLM behavior.
citing papers explorer
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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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LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans
Zero-shot LLM agents with human personas predict individual social media reactions better than chance (MCC 0.29) but worse than conventional text classifiers (MCC 0.36).
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Human Psychometric Questionnaires Mischaracterize LLM Behavior
Standard psychometric questionnaires like the Big Five and PVQ produce different and more consistent results than ecologically valid questions drawn from real user conversations, suggesting the former may mischaracterize LLM behavior.