Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues (2024)

Abstract: Personality recognition aims to identify the personality traits implied in user data such as dialogues and social media posts. Current research predominantly treats personality recognition as a classification task, failing to reveal the supporting evidence for the recognized personality. In this paper, we propose a novel task named Explainable Personality Recognition, aiming to reveal the reasoning process as supporting evidence of the personality trait. Inspired by personality theories, personality traits are made up of stable patterns of personality state, where the states are short-term characteristic patterns of thoughts, feelings, and behaviors in a concrete situation at a specific moment in time. We propose an explainable personality recognition framework called Chain-of-Personality-Evidence (CoPE), which involves a reasoning process from specific contexts to short-term personality states to long-term personality traits. Furthermore, based on the CoPE framework, we construct an explainable personality recognition dataset from dialogues, PersonalityEvd. We introduce two explainable personality state recognition and explainable personality trait recognition tasks, which require models to recognize the personality state and trait labels and their corresponding support evidence. Our extensive experiments based on Large Language Models on the two tasks show that revealing personality traits is very challenging and we present some insights for future research. We will release our dataset and source code to facilitate further studies in this direction.

## introduction personality, a characteristic way of thinking, feeling, and behaving @xcite , has a great impact on our lives, well-being, and health. therefore, identifying a person's personality has great potential in many real-world applications, such as human-computer interaction @xcite , psychological diagnosis and regulation @xcite , and job candidate screenings @xcite . traditional personality recognition methods typically depend on self-reported results from designed questionnaires. such an approach is not only time-consuming but also necessitates the cooperation of the subjects. therefore, automatic personality recognition (apr) has attracted increasing attention in recent years, which aims to predict one's personality based on user data. to support the apr research, various personality datasets have been proposed, such as the friendspersona dataset @xcite based on dialogues, the essays i (pennebaker and king, ... in dialogue 5, according to utterances u1, u2, u3, and u8, tang youyou showes excessive concern about her physical condition, insisting that she might have internal injuries this reflects that therefore in dialogue 6, according to utterances u1, u2, u3, u4, u5 and u8, tang youyou is very worried about her performance on saturday and even deliberately tries to make herself sick multiple times. this reflects that tang youyou tends to be anxious and worried. therefore, tang youyou's state level is high in neuroticism. (complete annotation) in dialogue 11, according to utterances u9, u10, and u12, tang youyou expresses dissatisfaction with the unpleasant ritual performance upstairs this reflects that therefore in terms of anxiety, tang youyou is often worried about her acting work, her physical health, guan gu's investment, yi fei's marriage, and other friends around her (d2, d5, d6, d7, d13, d14, d16, d17, d23) ; but she can encourage guan gu not to be afraid to become a teacher (d24). this reflects that tang youyou is easily nervous, often worried about many things. (complete annotation) in terms of depression, tang youyou thinks that everyone has their own characteristics, and there is no need to be depressed in terms of emotional volatility, tang youyou is very dissatisfied with the noise upstairs (d11) and angry about guan gu's in summary, it is judged that tang youyou's trait level is high in neuroticism. ## conclusion in this paper, to promote the research of explainable personality recognition, we propose a novel chainof-personality-evidence (cope) framework, which reveals the reasoning process from specific contexts to short-term personality states to long-term personality traits. we build an explainable personality dataset based on cope framework, namely personalityevd, which supports two explainable personality recognition tasks (epr-s and epr-t) that both require the model to recognize personality as well as provide corresponding evidence. finally, we evaluate several large language models as baselines and conduct extensive experiments on both tasks. additional human evaluations validate the quality of our constructed dataset and our analytical experiments present insights for future work. we hope that our personalityevd dataset and two novel tasks can facilitate further investigation into explainable personality analysis in the community.