  Accurate assessment of personality traits is crucial for effective
psycho-counseling, yet traditional methods like self-report questionnaires are
time-consuming and biased. This study exams whether Large Language Models
(LLMs) can predict the Big Five personality traits directly from counseling
dialogues and introduces an innovative framework to perform the task. Our
framework applies role-play and questionnaire-based prompting to condition LLMs
on counseling sessions, simulating client responses to the Big Five Inventory.
We evaluated our framework on 853 real-world counseling sessions, finding a
significant correlation between LLM-predicted and actual Big Five traits,
proving the validity of framework. Moreover, ablation studies highlight the
importance of role-play simulations and task simplification via questionnaires
in enhancing prediction accuracy. Meanwhile, our fine-tuned Llama3-8B model,
utilizing Direct Preference Optimization with Supervised Fine-Tuning, achieves
a 130.95\% improvement, surpassing the state-of-the-art Qwen1.5-110B by 36.94\%
in personality prediction validity. In conclusion, LLMs can predict personality
based on counseling dialogues. Our code and model are publicly available at
\url{https://github.com/kuri-leo/BigFive-LLM-Predictor}, providing a valuable
tool for future research in computational psychometrics.
