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
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

Predicting the Big Five Personality Traits in Chinese Counselling  Dialogues Using Large Language Models

An NLP model that understands stories should be able to understand the
characters in them. To support the development of neural models for this
purpose, we construct a benchmark, Story2Personality. The task is to predict a
movie character's MBTI or Big 5 personality types based on the narratives of
the character. Experiments show that our task is challenging for the existing
text classification models, as none is able to largely outperform random
guesses. We further proposed a multi-view model for personality prediction
using both verbal and non-verbal descriptions, which gives improvement compared
to using only verbal descriptions. The uniqueness and challenges in our dataset
call for the development of narrative comprehension techniques from the
perspective of understanding characters.

提出了 Story2Personality 数据集，该数据集主要用于预测电影角色的人格特质。实验证明，使用多视角模型可以比仅使用语言描述获得更高的人格预测准确率。

使用电影剧本对虚构人物进行 MBTI 人格预测

MBTI Personality Prediction for Fictional Characters Using Movie Scripts

This paper describes our proposed method for the Workshop on Computational
Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022
shared task on Personality Prediction (PER) and Reactivity Index Prediction
(IRI). In this paper, we adopt the prompt-based learning method with the
pre-trained language model to accomplish these tasks. Specifically, the prompt
is designed to provide knowledge of the extra personalized information for
enhancing the pre-trained model. Data augmentation and model ensemble are
adopted for obtaining better results. Moreover, we also provided the online
software demonstration and the codes of the software for further research.

利用预训练语言模型的提示学习方法，采用数据增强和模型组合，实现了计算机对主观性、情感和社交媒体分析领域的个性预测任务，同时提供了在线软件演示和代码的方便研究。