利用大语言模型智能体对公民面对官僚繁文缛节的情绪反应进行跨文化模拟 / Cross-Cultural Simulation of Citizen Emotional Responses to Bureaucratic Red Tape Using LLM Agents
1️⃣ 一句话总结
这篇论文研究发现,当前的大语言模型在模拟不同文化背景下公民对官僚繁文缛节的真实情绪反应时表现不佳,尤其是在东方文化中,为此提出了一个评估框架并开发了一个名为RAMO的交互工具来帮助改进模型。
Improving policymaking is a central concern in public administration. Prior human subject studies reveal substantial cross-cultural differences in citizens' emotional responses to red tape during policy implementation. While LLM agents offer opportunities to simulate human-like responses and reduce experimental costs, their ability to generate culturally appropriate emotional responses to red tape remains unverified. To address this gap, we propose an evaluation framework for assessing LLMs' emotional responses to red tape across diverse cultural contexts. As a pilot study, we apply this framework to a single red-tape scenario. Our results show that all models exhibit limited alignment with human emotional responses, with notably weaker performance in Eastern cultures. Cultural prompting strategies prove largely ineffective in improving alignment. We further introduce \textbf{RAMO}, an interactive interface for simulating citizens' emotional responses to red tape and for collecting human data to improve models. The interface is publicly available at this https URL.
利用大语言模型智能体对公民面对官僚繁文缛节的情绪反应进行跨文化模拟 / Cross-Cultural Simulation of Citizen Emotional Responses to Bureaucratic Red Tape Using LLM Agents
这篇论文研究发现,当前的大语言模型在模拟不同文化背景下公民对官僚繁文缛节的真实情绪反应时表现不佳,尤其是在东方文化中,为此提出了一个评估框架并开发了一个名为RAMO的交互工具来帮助改进模型。
源自 arXiv: 2604.12545