The process of opinion expression and exchange is a critical component of democratic societies. As people interact with large language models (LLMs) in the opinion shaping process different from traditional media, the impacts of LLMs are increasingly recognized and being concerned. However, the knowledge about how LLMs affect the process of opinion expression and exchange of social opinion networks is very limited. Here, we create an opinion network dynamics model to encode the opinions of LLMs, cognitive acceptability and usage strategies of individuals, and simulate the impact of LLMs on opinion dynamics in a variety of scenarios. The outcomes of the simulations inform about effective demand-oriented opinion network interventions. The results from this study suggested that the output opinion of LLMs has a unique and positive effect on the collective opinion difference. The marginal effect of cognitive acceptability on collective opinion formation is nonlinear and shows a decreasing trend. When people partially rely on LLMs, the exchange process of opinion becomes more intense and the diversity of opinion becomes more favorable. In fact, there is 38.6% more opinion diversity when people all partially rely on LLMs, compared to prohibiting the use of LLMs entirely. The optimal diversity of opinion was found when the fractions of people who do not use, partially rely on, and fully rely on LLMs reached roughly 4:12:1. Our experiments also find that introducing extra agents with opposite/neutral/random opinions, we can effectively mitigate the impact of biased/toxic output from LLMs. Our findings provide valuable insights into opinion dynamics in the age of LLMs, highlighting the need for customized interventions tailored to specific scenarios to address the drawbacks of improper output and use of LLMs.

翻译：意见表达与交流过程是民主社会的关键组成部分。当人们与传统媒体不同的方式在意见塑造过程中与大型语言模型（LLMs）交互时，LLMs的影响日益受到关注和重视。然而，关于LLMs如何影响社会意见网络的意见表达与交流过程的知识仍十分有限。在此，我们构建了一个意见网络动态模型，用以编码LLMs的意见、个体的认知接受度及使用策略，并在多种情景下模拟LLMs对意见动态的影响。模拟结果揭示了有效的需求导向型意见网络干预措施。研究结果表明，LLMs输出的意见对集体意见差异具有独特且正向的影响。认知接受度对集体意见形成的边际效应呈非线性且递减趋势。当人们部分依赖LLMs时，意见交流过程变得更加激烈，意见多样性也更为有利。事实上，与完全禁止使用LLMs相比，当所有人都部分依赖LLMs时，意见多样性增加了38.6%。当不使用、部分依赖和完全依赖LLMs的人群比例大致达到4:12:1时，意见多样性达到最优。我们的实验还发现，引入具有相反/中立/随机意见的额外智能体，可以有效缓解LLMs产生偏见/有害输出的影响。我们的发现为LLMs时代的意见动态提供了宝贵见解，强调了需要针对特定情景定制干预措施，以应对LLMs不当输出和使用带来的弊端。