观点过于开放？在大型语言模型中拥抱开放性以进行社会模拟
Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social Simulation
AI总结：
本文探讨了在大型语言模型中采用开放性文本进行社会模拟的重要性，强调其在提升测量、探索意外观点和减少偏差方面的价值。
摘要：
大型语言模型（LLMs）越来越多地用于模拟公众意见等社会现象。当前研究多采用多选或简答格式以方便评分和比较，但这种封闭设计忽视了LLMs固有的生成特性。本文主张在LLMs中使用自由文本捕捉话题、观点和推理过程，以实现更真实的社会模拟。基于数十年的调查方法研究和最新NLP进展，本文论证了开放性在LLMs社会模拟中的价值，展示了其如何改进测量与设计、支持探索意外观点、减少研究者主导偏差。它还捕捉了表达性和个体性，有助于预测试，并最终提升方法论效用。本文呼吁新的实践和评估框架，利用而非约束LLMs的开放生成多样性，促进NLP与社会科学之间的协同作用。
英文摘要：
Large Language Models (LLMs) are increasingly used to simulate public opinion and other social phenomena. Most current studies constrain these simulations to multiple-choice or short-answer formats for ease of scoring and comparison, but such closed designs overlook the inherently generative nature of LLMs. In this position paper, we argue that open-endedness, using free-form text that captures topics, viewpoints, and reasoning processes "in" LLMs, is essential for realistic social simulation. Drawing on decades of survey-methodology research and recent advances in NLP, we argue why this open-endedness is valuable in LLM social simulations, showing how it can improve measurement and design, support exploration of unanticipated views, and reduce researcher-imposed directive bias. It also captures expressiveness and individuality, aids in pretesting, and ultimately enhances methodological utility. We call for novel practices and evaluation frameworks that leverage rather than constrain the open-ended generative diversity of LLMs, creating synergies between NLP and social science.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
