arXiv:2507.05890v4 Announce Type: replace Abstract: As psychometric surveys are increasingly used to assess the traits of large language models (LLMs), the need for scalable survey item generation suited for LLMs has also grown. A critical challenge here is ensuring the construct validity of generated items, i.e., whether they truly measure the intended trait. Traditionally, this requires costly, large-scale human data collection. To make it efficient, we present a framework for virtual respondent simulation using LLMs. Our central idea is to account for mediators: factors through which the sa
The proliferation of LLMs and their increasing application in psychological assessment creates an urgent need for efficient and scalable psychometric validation methods.
This framework significantly reduces the cost and time associated with validating psychometric tools for AI, accelerating the development of more reliable and ethical AI systems.
The validation process for assessing AI traits becomes more automated and less reliant on arduous human data collection, shifting how 'trustworthiness' and 'capabilities' are measured for advanced models.
- · AI developers
- · Psychometricians
- · AI ethics and safety researchers
- · SaaS providers for AI assessment
- · Traditional human-centric psychometric data collection services
LLMs can be more rapidly and rigorously tested for various traits, leading to quicker iterations and improvements.
Standardized and automated validation could drive broader adoption and trust in AI systems that undergo such testing.
The methodology might eventually be adapted for validating psychometric tools for humans, leveraging AI for more efficient research.
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