Authors:
(1) Jinyu Cai, Waseda University (bluelink@toki.waseda.jp);
(2) Jialong Li, Waseda University (lijialong@fuji.waseda.jp);
(3) Mingyue Zhang, Southwest University (myzhangswu@swu.edu.cn);
(4) Munan Li, Dalian Maritime University (limunan@dlmu.edu.cn);
(5) Chen-Shu Wang, National Taipei University of Technology (wangcs@ntut.edu.tw);
(6) Kenji Tei, Tokyo Institute of Technology (tei@c.titech.ac.jp). Authors: Authors: (1) Jinyu Cai, Waseda University (bluelink@toki.waseda.jp); (2) Jialong Li, Waseda University (lijialong@fuji.waseda.jp); (3) Mingyue Zhang, Southwest University (myzhangswu@swu.edu.cn); (4) Munan Li, Dalian Maritime University (limunan@dlmu.edu.cn); (5) Chen-Shu Wang, National Taipei University of Technology (wangcs@ntut.edu.tw); (6) Kenji Tei, Tokyo Institute of Technology (tei@c.titech.ac.jp). Table of Links Abstract and I. Introduction Abstract and I. Introduction II. Background and Related Work II. Background and Related Work III. Framework Design III. Framework Design IV. Evaluation IV. Evaluation V. Conclusion and Future Work, Acknowledgement, and References V. Conclusion and Future Work, Acknowledgement, and References V. CONCLUSION AND FUTURE WORK Our study has introduced an LLM-based multi-agent simulation framework that effectively captures the nuanced strategies individuals use to bypass social media regulations. Through this framework, we have showcased LLMs’ proficiency in adapting communication tactics within regulated environments, reflecting the sophisticated dance between evolving language use and the constraints imposed by regulation. From abstract concepts to real-world scenarios, our research delineates the versatile capabilities of LLMs and underscores their significant potential to illuminate the pathways of language evolution in the digital realm. Nonetheless, it is crucial to consider that the linguistic adaptations observed in our simulations may not fully capture real human behaviors, and their applicability to other contexts remains uncertain. Moving forward, the scope of our research beckons a more intricate and comprehensive exploration. Future initiatives should aim to weave in complex interactional models, scale up the simulations to encompass broader user interaction networks, and incorporate dynamic, evolving regulatory frameworks to more accurately represent the fluidity of social media. Moreover, we envision incorporating human participants into the simulation framework, either as dialogue participants or supervisors, to conduct a more realistic evaluation. Furthermore, adopting a multimodal approach will more authentically capture the essence of social media, which blends textual, visual, and other forms of communication. These directions are anticipated to enhance the realism of our simulations, offering richer insights into language evolution tactics deployed to elude regulatory detection. ACKNOWLEDGEMENT This study was partially supported by the Pioneering Research Program for a Waseda Open Innovation Ecosystem (W-SPRING), and the Special Research Projects of Waseda University (Grant Number 2024E-021). 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Can GPT Outsmart Social Media Regulations? Inside an AI Language Evolution Experiment

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