社会拟像:为社会计算系统创建填充原型

J. Park, Lindsay Popowski, Carrie J. Cai, M. Morris, Percy Liang, Michael S. Bernstein
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引用次数: 69

摘要

社会计算原型探索在设想的系统设计中可能出现的社会行为。这种原型实践目前仅限于招募一小群人。不幸的是,直到系统被大规模地填充后,许多挑战才会出现。设计师能否理解一个社会系统在人口密集时的表现,并在系统陷入这种挑战之前对设计做出调整?我们将介绍社会拟像,这是一种原型技术,可以生成在社会计算系统被填充时可能出现的广泛的现实社会交互。社交模拟将设计师对社区设计目标、规则和成员角色的描述作为输入,并通过模拟行为(包括帖子、回复和反社会行为)生成该设计的实例作为输出。我们证明,社交模拟改变了它们产生的行为,以适当地响应设计变化,并且它们允许探索“如果?”社区成员或版主干预的场景。为了增强社交拟像,我们贡献了一些技术来促进一个大型语言模型来生成数千个不同的社区成员和他们之间的社会互动;这些技术是通过观察到大型语言模型的训练数据已经包括社交媒体平台上各种各样的积极和消极行为而实现的。在评估中,我们表明参与者通常无法区分社会模拟和实际社区行为,而社会计算设计者在使用社会模拟时成功地改进了他们的社会计算设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social Simulacra: Creating Populated Prototypes for Social Computing Systems
Social computing prototypes probe the social behaviors that may arise in an envisioned system design. This prototyping practice is currently limited to recruiting small groups of people. Unfortunately, many challenges do not arise until a system is populated at a larger scale. Can a designer understand how a social system might behave when populated, and make adjustments to the design before the system falls prey to such challenges? We introduce social simulacra, a prototyping technique that generates a breadth of realistic social interactions that may emerge when a social computing system is populated. Social simulacra take as input the designer’s description of a community’s design—goal, rules, and member personas—and produce as output an instance of that design with simulated behavior, including posts, replies, and anti-social behaviors. We demonstrate that social simulacra shift the behaviors that they generate appropriately in response to design changes, and that they enable exploration of “what if?” scenarios where community members or moderators intervene. To power social simulacra, we contribute techniques for prompting a large language model to generate thousands of distinct community members and their social interactions with each other; these techniques are enabled by the observation that large language models’ training data already includes a wide variety of positive and negative behavior on social media platforms. In evaluations, we show that participants are often unable to distinguish social simulacra from actual community behavior and that social computing designers successfully refine their social computing designs when using social simulacra.
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