A Survey of Large Language Models for Perception and Measurement of Human Psychology

Abstract

Against the backdrop of the rapid advancement of Large Language Models (LLMs), their application in the field of psychology has garnered significant academic attention. A central issue is whether LLMs possess the capability to accurately perceive and measure complex, latent human psychological constructs, such as personality, emotions, and cognitive states. This paper provides a systematic review focused on the use of LLMs as instruments for human psychological measurement. To organize this domain, we propose a comprehensive analytical framework structured around three critical dimensions: Theoretical Plausibility (why measurement might be possible), Measurement Methodology (how to measure), and Application Effectiveness (what has been measured). We first explore the theoretical foundations supporting LLM-based measurement, examining the debate on their emergent cognitive properties from a psychometric perspective. Next, we systematically analyze existing measurement paradigms, categorizing them into active conversational assessment, passive natural language analysis, and multimodal fusion. Subsequently, we review the practical effectiveness and limitations of LLMs in core application areas, including personality trait assessment and mental health evaluation. Distinct from prior reviews focusing on general applications or the ``psychology'' of LLMs themselves, this paper centers on the psychometric properties of LLMs as measurement tools.