AFSPP：基于大语言模型塑造偏好与个性的智能体框架
AFSPP: Agent Framework for Shaping Preference and Personality with Large Language Models
AI总结：
本文提出AFSPP框架，探索社交网络和主观意识对基于LLM的智能体偏好与个性形成的影响，成功复现人类个性实验关键发现，并提升心理实验效率，为可信AI防范不良偏好提供见解。
摘要：
大语言模型（LLMs）的演进为研究人类行为模拟引入了新范式。近期研究采用基于LLM的智能体构建社会学研究环境，其中智能体表现出大语言模型未经处理的特征。然而，这些研究忽略了类人环境中的迭代发展过程——人类的偏好与个性十分复杂，由多种因素塑造，并会因环境和主观影响而持续变化。鉴于这一观察，我们提出了塑造偏好与个性的智能体框架（AFSPP），探索社交网络和主观意识对基于LLM的智能体偏好与个性形成的多方面影响。借助AFSPP，我们首次成功复现了人类个性实验中的几项关键发现。其他基于AFSPP的实验结果表明，计划制定、感知能力以及带有主观信息的社交网络，对偏好塑造的影响最为显著。AFSPP能显著提升心理实验的效率与范围，同时为可信人工智能研究提供策略上的宝贵见解，以防止不良偏好与个性的发展。
英文摘要：
The evolution of Large Language Models (LLMs) has introduced a new paradigm for investigating human behavior emulation. Recent research has employed LLM-based Agents to create a sociological research environment, in which agents exhibit behavior based on the unfiltered characteristics of large language models. However, these studies overlook the iterative development within a human-like setting - Human preferences and personalities are complex, shaped by various factors and subject to ongoing change as a result of environmental and subjective influences. In light of this observation, we propose Agent Framework for Shaping Preference and Personality (AFSPP), exploring the multifaceted impact of social networks and subjective consciousness on LLM-based Agents' preference and personality formation. With AFSPP, we have, for the first time, successfully replicated several key findings from human personality experiments. And other AFSPP-based experimental results indicate that plan making, sensory perceptions and social networking with subjective information, wield the most pronounced influence on preference shaping. AFSPP can significantly enhance the efficiency and scope of psychological experiments, while yielding valuable insights for Trustworthy Artificial Intelligence research for strategies to prevent undesirable preference and personality development.
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。
