  In the field of psychology, the static nature and lack of customization of
psychological test scales, along with the challenge of quantifying
psychological indicators, have long been critical issues. Despite numerous
attempts to use AI to address psychological challenges, a dynamically
interactive psychological test has yet to emerge. In contrast to traditional
psychological assessment methods, we propose PsyDI, a multi-modal, interactive,
and customized chatbot for psychological assessments, using the Myers-Briggs
Type Indicator (MBTI) as an example. PsyDI initiates with user-related
multi-modal information, then engaging in customized interaction to discern the
user's MBTI type based on their multiple rounds of responses. Despite these
advancements, accurately quantifying absolute value of psychological indicators
remains challenging. To tackle such difficulty, we introduce the PsyDI
framework that trains LLMs to discern the relative magnitude of psychological
traits rather than their absolute values. Through various experiments, we
demonstrate the effectiveness of the training techniques proposed in PsyDI on
various datasets, and we have also launched its web version, reaching about ~3k
accesses. Additionally, comprehensive post-deployment data analysis has
provided profound insights into the implications and applications of PsyDI,
demonstrating its potential to serve as a general framework for psychological
assessment.
