利用基于LLM的智能体网络模拟观点动态
Simulating Opinion Dynamics with Networks of LLM-based Agents
AI总结：
本文提出用大语言模型智能体群体模拟观点动态，发现其固有准确性偏置导致过早共识，而通过提示工程引入确认偏误后可复现观点分裂，为改进LLM以模拟人类信念演化提供了方向。
摘要：
准确模拟人类观点动态对于理解包括极化和错误信息传播在内的各种社会现象至关重要。然而，常用于此类模拟的基于智能体的模型（ABMs）往往过度简化了人类行为。我们提出了一种基于大语言模型（LLMs）群体的新方法来模拟观点动态。我们的研究发现，LLM智能体存在强烈的固有偏置，倾向于产生准确信息，导致模拟智能体在与科学现实一致的方面达成共识。这种偏置限制了它们在理解诸如气候变化等问题上对共识观点抵制方面的效用。然而，在通过提示工程诱导确认偏误后，我们观察到了与现有基于智能体的建模和观点动态研究一致的观点分裂现象。这些见解突显了LLM智能体在该领域的潜力与局限，并指出了前进的方向：利用现实世界话语来精炼LLMs，以更好地模拟人类信念的演化。
英文摘要：
Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation. However, the agent-based models (ABMs) commonly used for such simulations often over-simplify human behavior. We propose a new approach to simulating opinion dynamics based on populations of Large Language Models (LLMs). Our findings reveal a strong inherent bias in LLM agents towards producing accurate information, leading simulated agents to consensus in line with scientific reality. This bias limits their utility for understanding resistance to consensus views on issues like climate change. After inducing confirmation bias through prompt engineering, however, we observed opinion fragmentation in line with existing agent-based modeling and opinion dynamics research. These insights highlight the promise and limitations of LLM agents in this domain and suggest a path forward: refining LLMs with real-world discourse to better simulate the evolution of human beliefs.
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