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

TL;DR

This paper introduces a new benchmark dataset and framework for explainable personality recognition from dialogues, emphasizing the reasoning process behind identifying personality traits and states.

Contribution

It proposes the Chain-of-Personality-Evidence framework and constructs the first explainable personality recognition dataset from dialogues, enabling models to provide supporting evidence.

Findings

Revealing personality traits is challenging for current models.

Large Language Models can recognize personality states and traits with some success.

The dataset and framework facilitate future research in explainable personality recognition.

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. Our data and code are available at https://github.com/Lei-Sun-RUC/PersonalityEvd.