Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues
- URL: http://arxiv.org/abs/2409.19723v1
- Date: Sun, 29 Sep 2024 14:41:43 GMT
- Title: Revealing Personality Traits: A New Benchmark Dataset for Explainable Personality Recognition on Dialogues
- Authors: Lei Sun, Jinming Zhao, Qin Jin,
- Abstract summary: Personality recognition aims to identify the personality traits implied in user data such as dialogues and social media posts.
We propose a novel task named Explainable Personality Recognition, aiming to reveal the reasoning process as supporting evidence of the personality trait.
- Score: 63.936654900356004
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Is persona enough for personality? Using ChatGPT to reconstruct an agent's latent personality from simple descriptions [2.6080756513915824]
Personality, a fundamental aspect of human cognition, contains a range of traits that influence behaviors, thoughts, and emotions.
This paper explores the capabilities of large language models (LLMs) in reconstructing these complex cognitive attributes based only on simple descriptions containing socio-demographic and personality type information.
arXiv Detail & Related papers (2024-06-18T02:32:57Z) - Enhancing Textual Personality Detection toward Social Media: Integrating Long-term and Short-term Perspectives [21.548313630700033]
Textual personality detection aims to identify personality characteristics by analyzing user-generated content toward social media platforms.
Recent literature highlighted that personality encompasses both long-term stable traits and short-term dynamic states.
arXiv Detail & Related papers (2024-04-23T14:13:53Z) - Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation [30.820334868031537]
Personality Recognition in Conversation (PRC) aims to identify the personality traits of speakers through textual dialogue content.
We propose Affective Natural Language Inference (Affective-NLI) for accurate and interpretable PRC.
arXiv Detail & Related papers (2024-04-03T09:14:24Z) - LLMvsSmall Model? Large Language Model Based Text Augmentation Enhanced
Personality Detection Model [58.887561071010985]
Personality detection aims to detect one's personality traits underlying in social media posts.
Most existing methods learn post features directly by fine-tuning the pre-trained language models.
We propose a large language model (LLM) based text augmentation enhanced personality detection model.
arXiv Detail & Related papers (2024-03-12T12:10:18Z) - PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for
Personality Detection [50.66968526809069]
We propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner.
Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection.
arXiv Detail & Related papers (2023-10-31T08:23:33Z) - InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews [57.04431594769461]
This paper introduces a novel perspective to evaluate the personality fidelity of RPAs with psychological scales.
Experiments include various types of RPAs and LLMs, covering 32 distinct characters on 14 widely used psychological scales.
With InCharacter, we show that state-of-the-art RPAs exhibit personalities highly aligned with the human-perceived personalities of the characters, achieving an accuracy up to 80.7%.
arXiv Detail & Related papers (2023-10-27T08:42:18Z) - Editing Personality for Large Language Models [73.59001811199823]
This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs)
We construct PersonalityEdit, a new benchmark dataset to address this task.
arXiv Detail & Related papers (2023-10-03T16:02:36Z) - Identifying and Manipulating the Personality Traits of Language Models [9.213700601337383]
We investigate whether perceived personality in language models is exhibited consistently in their language generation.
We show that language models such as BERT and GPT2 can consistently identify and reflect personality markers in different contexts.
This behavior illustrates an ability to be manipulated in a highly predictable way, and frames them as tools for identifying personality traits and controlling personas in applications such as dialog systems.
arXiv Detail & Related papers (2022-12-20T14:24:11Z) - Domain-specific Learning of Multi-scale Facial Dynamics for Apparent
Personality Traits Prediction [3.19935268158731]
We propose a novel video-based automatic personality traits recognition approach.
It consists of: (1) a textbfdomain-specific facial behavior modelling module that extracts personality-related multi-scale short-term human facial behavior features; (2) a textbflong-term behavior modelling module that summarizes all short-term features of a video as a long-term/video-level personality representation; and (3) a textbfmulti-task personality traits prediction module that models underlying relationship among all traits and jointly predict them based on the video-level personality representation.
arXiv Detail & Related papers (2022-09-09T07:08:55Z) - Vyaktitv: A Multimodal Peer-to-Peer Hindi Conversations based Dataset
for Personality Assessment [50.15466026089435]
We present a novel peer-to-peer Hindi conversation dataset- Vyaktitv.
It consists of high-quality audio and video recordings of the participants, with Hinglish textual transcriptions for each conversation.
The dataset also contains a rich set of socio-demographic features, like income, cultural orientation, amongst several others, for all the participants.
arXiv Detail & Related papers (2020-08-31T17:44:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.