Large language models (LLMs) have demonstrated an impressive ability to role-play humans and replicate complex social dynamics. While large-scale social simulations are gaining increasing attention, they still face significant challenges, particularly regarding high time and computation costs. Existing solutions, such as distributed mechanisms or hybrid agent-based model (ABM) integrations, either fail to address inference costs or compromise accuracy and generalizability. To this end, we propose EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation. EcoLANG operates in two stages: (1) language evolution, where we filter synonymous words and optimize sentence-level rules through natural selection, and (2) language utilization, where agents in social simulations communicate using the evolved language. Experimental results demonstrate that EcoLANG reduces token consumption by over 20%, enhancing efficiency without sacrificing simulation accuracy.
View on arXiv@article{mou2025_2505.06904,
title={ EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation },
author={ Xinyi Mou and Chen Qian and Wei Liu and Xuanjing Huang and Zhongyu Wei },
journal={arXiv preprint arXiv:2505.06904},
year={ 2025 }
}