This paper introduces ElecTwit, a simulation framework designed to study persuasion within multi-agent systems, specifically emulating the interactions on social media platforms during a political election. By grounding our experiments in a realistic environment, we aimed to overcome the limitations of game-based simulations often used in prior research. We observed the comprehensive use of 25 specific persuasion techniques across most tested LLMs, encompassing a wider range than previously reported. The variations in technique usage and overall persuasion output between models highlight how different model architectures and training can impact the dynamics in realistic social simulations. Additionally, we observed unique phenomena such as "kernel of truth" messages and spontaneous developments with an "ink" obsession, where agents collectively demanded written proof. Our study provides a foundation for evaluating persuasive LLM agents in real-world contexts, ensuring alignment and preventing dangerous outcomes.

翻译：本文介绍了ElecTwit，这是一个旨在研究多智能体系统内说服行为的仿真框架，特别模拟了政治选举期间社交媒体平台上的互动。通过将实验建立在现实环境中，我们旨在克服先前研究中常用的基于博弈的模拟的局限性。我们观察到，在大多数测试的大型语言模型中，全面使用了25种特定的说服技巧，涵盖的范围比以往报道的更广。不同模型在技巧使用和整体说服输出上的差异，凸显了不同的模型架构和训练如何影响现实社会模拟中的动态。此外，我们观察到了一些独特现象，例如“真相内核”信息和自发性发展的“墨水”痴迷，即智能体集体要求书面证据。我们的研究为在现实世界情境中评估具有说服力的大型语言模型智能体奠定了基础，以确保其对齐性并防止危险后果。