Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data

• URL: http://arxiv.org/abs/2406.18921v3
• Date: Tue, 15 Oct 2024 14:24:56 GMT
• Title: Capturing Minds, Not Just Words: Enhancing Role-Playing Language Models with Personality-Indicative Data
• Authors: Yiting Ran, Xintao Wang, Rui Xu, Xinfeng Yuan, Jiaqing Liang, Deqing Yang, Yanghua Xiao,
• Abstract summary: We propose to enhance role-playing language models (RPLMs) via personality-indicative data. Specifically, we leverage questions from psychological scales and distill advanced RPAs to generate dialogues that grasp the minds of characters. Experimental results validate that RPLMs trained with our dataset exhibit advanced role-playing capabilities for both general and personality-related evaluations.
• Score: 58.92110996840019
• License:
• Abstract: Role-playing agents (RPA) have been a popular application area for large language models (LLMs), attracting significant interest from both industry and academia.While existing RPAs well portray the characters' knowledge and tones, they face challenges in capturing their minds, especially for small role-playing language models (RPLMs). In this paper, we propose to enhance RPLMs via personality-indicative data. Specifically, we leverage questions from psychological scales and distill advanced RPAs to generate dialogues that grasp the minds of characters. Experimental results validate that RPLMs trained with our dataset exhibit advanced role-playing capabilities for both general and personality-related evaluations. Code and data are available at \href{https://github.com/alienet1109/RolePersonality}{this URL}.

Related papers

• A Multi-Task Role-Playing Agent Capable of Imitating Character Linguistic Styles [28.237927070779925]
  Current Role-Playing Agents (RPAs) predominantly focus on mimicking a character's fundamental attributes while neglecting the replication of linguistic style. We develop StyleRPA, a Multi-Task Role-Playing Agent (MRPA) that significantly outperforms recent open-source LLMs and RPAs baselines on 7 tasks including Dialogue, Dictionary, Composition, Story Generation, Product Description, Music Commentary, and Open Question Answering.
  arXiv  Detail & Related papers  (2024-11-04T02:26:27Z)
• Thinking Before Speaking: A Role-playing Model with Mindset [0.6428333375712125]
  Large Language Models (LLMs) are skilled at simulating human behaviors. These models tend to perform poorly when confronted with knowledge that the assumed role does not possess. We propose a Thinking Before Speaking (TBS) model in this paper.
  arXiv  Detail & Related papers  (2024-09-14T02:41:48Z)
• MINDECHO: Role-Playing Language Agents for Key Opinion Leaders [50.43050502970816]
  This paper introduces MINDECHO, a framework for the development and evaluation of Key Opinion Leaders (KOLs) MINDECHO collects KOL data from Internet video transcripts in various professional fields, and synthesizes their conversations leveraging GPT-4. Our evaluation covers both general dimensions (ie, knowledge and tones) and fan-centric dimensions for KOLs.
  arXiv  Detail & Related papers  (2024-07-07T09:08:33Z)
• From Persona to Personalization: A Survey on Role-Playing Language Agents [52.783043059715546]
  Recent advancements in large language models (LLMs) have boosted the rise of Role-Playing Language Agents (RPLAs) RPLAs achieve a remarkable sense of human likeness and vivid role-playing performance. They have catalyzed numerous AI applications, such as emotional companions, interactive video games, personalized assistants and copilots.
  arXiv  Detail & Related papers  (2024-04-28T15:56:41Z)
• Large Language Models are Superpositions of All Characters: Attaining Arbitrary Role-play via Self-Alignment [62.898963074989766]
  We introduce Ditto, a self-alignment method for role-play. This method creates a role-play training set comprising 4,000 characters, surpassing the scale of currently available datasets by tenfold. We present the first comprehensive cross-supervision alignment experiment in the role-play domain.
  arXiv  Detail & Related papers  (2024-01-23T03:56:22Z)
• RoleCraft-GLM: Advancing Personalized Role-Playing in Large Language Models [6.753588449962107]
  RoleCraft-GLM is an innovative framework aimed at enhancing personalized role-playing with Large Language Models (LLMs) We contribute a unique conversational dataset that shifts from conventional celebrity-centric characters to diverse, non-celebrity personas. Our approach includes meticulous character development, ensuring dialogues are both realistic and emotionally resonant.
  arXiv  Detail & Related papers  (2023-12-17T17:57:50Z)
• 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)
• Character-LLM: A Trainable Agent for Role-Playing [67.35139167985008]
  Large language models (LLMs) can be used to serve as agents to simulate human behaviors. We introduce Character-LLM that teach LLMs to act as specific people such as Beethoven, Queen Cleopatra, Julius Caesar, etc.
  arXiv  Detail & Related papers  (2023-10-16T07:58:56Z)

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.