arXiv:2601.14264v2 Announce Type: replace-cross Abstract: Large language models (LLMs) act as digital twins for human respondents, yet their psychometric comparability remains uncertain. We propose a construct validity framework spanning construct representation and the nomothetic span, benchmarking models against human gold standards. Across studies, digital twins achieved high aggregate-level accuracy and profile correlations, but showed attenuated item-level correlations. In word association tests, LLM networks exhibited humanlike small-world structure and theory-consistent communities, yet
The proliferation of advanced LLMs and the increasing drive to understand their capabilities and limitations in mimicking human cognition make the psychometric comparability a critical research area now.
Understanding the psychometric accuracy of LLM digital twins is crucial for developing reliable AI agents, synthetic populations for research, and ultimately, robust AI with human-like understanding.
This research provides a framework for evaluating LLMs as digital twins, moving beyond simple task performance to assessing their internal cognitive representations and behavioral consistency with human models.
- · AI researchers
- · Psychometrics
- · AI ethicists
- · Synthetic data providers
- · Uncritically deployed LLM applications
- · Studies relying on simplistic LLM psychometric assumptions
Improved reliability and safety metrics for LLM applications that simulate human behavior.
The development of more sophisticated AI agents capable of nuanced human interaction and decision-making.
Potential for AI-generated personas to fully replace human participants in certain social science and market research studies.
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Read at arXiv cs.AI