What if LLMs Have Different World Views: Simulating Alien Civilizations with LLM-based Agents
<|reference_start|>What if LLMs Have Different World Views: Simulating Alien Civilizations with LLM-based Agents: In this study, we introduce "CosmoAgent," an innovative artificial intelligence framework utilizing Large Language Models (LLMs) to simulate complex interactions between human and extraterrestrial civilizations, with a special emphasis on Stephen Hawking's cautionary advice about not sending radio signals haphazardly into the universe. The goal is to assess the feasibility of peaceful coexistence while considering potential risks that could threaten well-intentioned civilizations. Employing mathematical models and state transition matrices, our approach quantitatively evaluates the development trajectories of civilizations, offering insights into future decision-making at critical points of growth and saturation. Furthermore, the paper acknowledges the vast diversity in potential living conditions across the universe, which could foster unique cosmologies, ethical codes, and worldviews among various civilizations. Recognizing the Earth-centric bias inherent in current LLM designs, we propose the novel concept of using LLMs with diverse ethical paradigms and simulating interactions between entities with distinct moral principles. This innovative research provides a new way to understand complex inter-civilizational dynamics, expanding our perspective while pioneering novel strategies for conflict resolution, which are crucial for preventing interstellar conflicts. We have also released the code and datasets to enable further academic investigation into this interesting area of research. The code is available at https://github.com/MingyuJ666/Simulating-Alien-Civilizations-with-LLM-based-Agents.<|reference_end|>
@article{jin2024what,
    title={What if LLMs Have Different World Views: Simulating Alien Civilizations
  with LLM-based Agents},
    author={Mingyu Jin, Beichen Wang, Zhaoqian Xue, Suiyuan Zhu, Wenyue Hua, Hua
  Tang, Kai Mei, Mengnan Du, Yongfeng Zhang},
    journal={arXiv preprint arXiv:2402.13184},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13184},
    primaryClass={cs.CL}
}