On the Reliability of Psychological Scales on Large Language Models
Abstract: Recent research has extended beyond assessing the performance of Large Language Models (LLMs) to examining their characteristics from a psychological standpoint, acknowledging the necessity of understanding their behavioral characteristics. The administration of personality tests to LLMs has emerged as a noteworthy area in this context. However, the suitability of employing psychological scales, initially devised for humans, on LLMs is a matter of ongoing debate. Our study aims to determine the reliability of applying personality assessments to LLMs, explicitly investigating whether LLMs demonstrate consistent personality traits. Analyzing responses under 2,500 settings reveals that various LLMs show consistency in responses to the Big Five Inventory, indicating a high degree of reliability. Furthermore, our research explores the potential of gpt-3.5-turbo to emulate diverse personalities and represent various groups—a capability increasingly sought after in social sciences for substituting human participants with LLMs to reduce costs. Our findings reveal that LLMs have the potential to represent different personalities with specific prompt instructions.
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: Psychological Scales, Large Language Models, Personality
Contribution Types: Model analysis & interpretability
Languages Studied: English (En), Chinese (Zh), Spanish (Es), French (Fr), German (De), Italian (It), Arabic (Ar), Russian (Ru), Japanese (Ja)
Submission Number: 5130
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