Beyond pretend-reality dualism: frame analysis of LLM-powered role play with social agents

  • Role-playing activities offer opportunities for developing individuals’ creativity, communication, and problem-solving skills. Recent advances in large language models (LLM) facilitate fluent conversations with machines. To investigate benefits and pitfalls of LLMs in a relatively unexplored context of human-agent role-play as a culturally contextualised activity, a dataset of twelve human-agent interactions produced by two researchers with two state-of theart LLMs was annotated based on a frame analysis scheme from literature. The pilot study shows that human-agent play has a similar complexity as human human play in which players maintain identities of themselves, external observers and play characters simultaneously going beyond the pretend-reality dualism. Results suggest that, while the LLMs can maintain and shift between roles, they play some roles better than others, and display cultural and gender stereotypes. Additionally, the coding scheme shows potential to help identify LLMRole-playing activities offer opportunities for developing individuals’ creativity, communication, and problem-solving skills. Recent advances in large language models (LLM) facilitate fluent conversations with machines. To investigate benefits and pitfalls of LLMs in a relatively unexplored context of human-agent role-play as a culturally contextualised activity, a dataset of twelve human-agent interactions produced by two researchers with two state-of theart LLMs was annotated based on a frame analysis scheme from literature. The pilot study shows that human-agent play has a similar complexity as human human play in which players maintain identities of themselves, external observers and play characters simultaneously going beyond the pretend-reality dualism. Results suggest that, while the LLMs can maintain and shift between roles, they play some roles better than others, and display cultural and gender stereotypes. Additionally, the coding scheme shows potential to help identify LLM outputs that require embodied enactment, and to be used for LLM bench-marking for role-play.show moreshow less

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Metadaten
Author:Sviatlana Höhn, Jauwairia NasirORCiDGND, Daniel C. Tozadore, Ali Paikan, Pouyan Ziafati, Elisabeth AndréORCiDGND
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/118923
ISBN:979-8-4007-1178-7OPAC
Parent Title (English):HAI'24: proceedings of the 12th International Conference on Human-Agent Interaction, Swansea, United Kingdom, November 24-27, 2024
Publisher:Association for Computing Machinery (ACM)
Place of publication:New York, NY
Editor:Muneeb Imtiaz Ahmad, Katrin Lohan, Mary Ellen Foster, Patrick Holthaus, Yukie Nagai
Type:Conference Proceeding
Language:English
Year of first Publication:2024
Release Date:2025/02/10
First Page:393
Last Page:395
DOI:https://doi.org/10.1145/3687272.3690894
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Menschzentrierte Künstliche Intelligenz
Nachhaltigkeitsziele
Nachhaltigkeitsziele / Ziel 5 - Geschlechtergleichheit
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit