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.

大型语言模型(LLMs)可以通过咨询对话预测个性特质。通过在咨询过程中运用角色扮演和基于问卷的提示，该研究提出了一种创新框架来实现这一目标。在实际咨询场景中，通过853个真实咨询对话的评估，发现LLM预测的个性特质与实际结果存在显著相关性，证明了该框架的有效性。同时，通过消融研究，强调了角色扮演模拟和通过问卷简化任务对提高预测准确性的重要性。此外，我们的经过精调的Llama3-8B模型在个性预测有效性方面实现了130.95%的改进，超过了Qwen1.5-110B的36.94%。该研究提供了一个公开可用的代码和模型，为计算心理测量学的未来研究提供了有价值的工具。

利用大型语言模型预测中国辅导对话中的五大人格特征