用检索增强生成模拟社交网络，让虚拟用户对话更自然。
Retrieval-Augmented Simulacra: Generative Agents for Up-to-date and Knowledge-Adaptive Simulations
- 引入检索增强机制，模仿人类搜索行为生成内容。
- 相比基线方法，生成对话更贴近真实社交网络交互。
- 适合研究社交媒体趋势或构建动态虚拟社区的学者。
2023年信息通信白皮书预测，日本社交网络服务用户数将在2022年超过1亿，其影响力持续增长。当前，利用SNS进行营销及研究情绪与信息传播的活动日益活跃，亟需可预测SNS互动趋势的系统。我们已构建一个虚拟SNS环境，通过大型语言模型驱动的代理在聊天社区中相互发帖与回复，模拟各类社区行为。本文评估了在该虚拟环境中，采用检索增强生成机制生成帖子与回复对交互自然度的影响。结果表明，该机制因模仿人类搜索行为，能生成最自然的对话交流。
原文摘要 · Abstract (English)
In the 2023 edition of the White Paper on Information and Communications, it is estimated that the population of social networking services in Japan will exceed 100 million by 2022, and the influence of social networking services in Japan is growing significantly. In addition, marketing using SNS and research on the propagation of emotions and information on SNS are being actively conducted, creating the need for a system for predicting trends in SNS interactions. We have already created a system that simulates the behavior of various communities on SNS by building a virtual SNS environment in which agents post and reply to each other in a chat community created by agents using a LLMs. In this paper, we evaluate the impact of the search extension generation mechanism used to create posts and replies in a virtual SNS environment using a simulation system on the ability to generate posts and replies. As a result of the evaluation, we confirmed that the proposed search extension generation mechanism, which mimics human search behavior, generates the most natural exchange.
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