@inproceedings{lin-etal-2025-creativity,
title = "Creativity in {LLM}-based Multi-Agent Systems: A Survey",
author = "Lin, Yi-Cheng and
Chen, Kang-Chieh and
Li, Zhe-Yan and
Wu, Tzu-Heng and
Wu, Tzu-Hsuan and
Chen, Kuan-Yu and
Lee, Hung-yi and
Chen, Yun-Nung",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1403/",
pages = "27572--27595",
ISBN = "979-8-89176-332-6",
abstract = "Large language model (LLM)-driven multi-agent systems (MAS) are transforming how humans and AIs collaboratively generate ideas and artifacts. While existing surveys provide comprehensive overviews of MAS infrastructures, they largely overlook the dimension of \textit{creativity}, including how novel outputs are generated and evaluated, how creativity informs agent personas, and how creative workflows are coordinated. This is the first survey dedicated to creativity in MAS. We focus on text and image generation tasks, and present:(1) a taxonomy of agent proactivity and persona design;(2) an overview of generation techniques, including divergent exploration, iterative refinement, and collaborative synthesis, as well as relevant datasets and evaluation metrics; and(3) a discussion of key challenges, such as inconsistent evaluation standards, insufficient bias mitigation, coordination conflicts, and the lack of unified benchmarks.This survey offers a structured framework and roadmap for advancing the development, evaluation, and standardization of creative MAS."
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%0 Conference Proceedings
%T Creativity in LLM-based Multi-Agent Systems: A Survey
%A Lin, Yi-Cheng
%A Chen, Kang-Chieh
%A Li, Zhe-Yan
%A Wu, Tzu-Heng
%A Wu, Tzu-Hsuan
%A Chen, Kuan-Yu
%A Lee, Hung-yi
%A Chen, Yun-Nung
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F lin-etal-2025-creativity
%X Large language model (LLM)-driven multi-agent systems (MAS) are transforming how humans and AIs collaboratively generate ideas and artifacts. While existing surveys provide comprehensive overviews of MAS infrastructures, they largely overlook the dimension of creativity, including how novel outputs are generated and evaluated, how creativity informs agent personas, and how creative workflows are coordinated. This is the first survey dedicated to creativity in MAS. We focus on text and image generation tasks, and present:(1) a taxonomy of agent proactivity and persona design;(2) an overview of generation techniques, including divergent exploration, iterative refinement, and collaborative synthesis, as well as relevant datasets and evaluation metrics; and(3) a discussion of key challenges, such as inconsistent evaluation standards, insufficient bias mitigation, coordination conflicts, and the lack of unified benchmarks.This survey offers a structured framework and roadmap for advancing the development, evaluation, and standardization of creative MAS.
%U https://aclanthology.org/2025.emnlp-main.1403/
%P 27572-27595
Markdown (Informal)
[Creativity in LLM-based Multi-Agent Systems: A Survey](https://aclanthology.org/2025.emnlp-main.1403/) (Lin et al., EMNLP 2025)
ACL
- Yi-Cheng Lin, Kang-Chieh Chen, Zhe-Yan Li, Tzu-Heng Wu, Tzu-Hsuan Wu, Kuan-Yu Chen, Hung-yi Lee, and Yun-Nung Chen. 2025. Creativity in LLM-based Multi-Agent Systems: A Survey. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 27572–27595, Suzhou, China. Association for Computational Linguistics.