发表机构
Carnegie Mellon University; North Carolina State University; University of Hong Kong（卡内基梅隆大学; 北卡罗来纳州立大学; 香港大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过范围综述58项研究，系统梳理了面向单用户的LLM助手在多方情境中的五个操作路径、五类风险及五类应对措施，并提出关注角色变化和保持人物边界的治理与设计建议。
AI 中文摘要
基于LLM的助手正日益扩展到多方参与的情境中，而其用于上下文管理、个性化、身份归属、权限归属和行动执行的核心操作流程往往仍围绕单个用户组织。现有研究考察了特定的多方情境，但缺乏对这些面向单用户的助手如何开始涉及额外的人类参与方以及会出现哪些风险的系统性阐述。为填补这一空白，我们对58项研究进行了范围综述。我们识别出涵盖直接和间接参与的五个操作路径、五个反复出现的风险领域，以及五个已实施和拟议的应对领域。基于这些发现，我们主张治理应关注实践中跨人物角色和关系的动态变化，并主张助手设计应在整个交互过程中保持针对特定人物的边界。
英文摘要
LLM-based assistants are increasingly extending into multi-party contexts, while core operational processes for context management, personalization, identity attribution, authority attribution, and action execution often remain organized around a single user. Existing work examines particular multi-party settings, but lacks a systematic account of how these single-user-oriented assistants begin to involve additional human parties and what risks emerge. To address this gap, we conducted a scoping review of 58 studies. We identify five operational pathways spanning direct and indirect involvement, five recurring risk domains, and five areas of implemented and proposed responses. Based on these findings, we argue for governance that attends to changing cross-person roles and relationships in practice, and for assistant designs that preserve person-specific boundaries throughout interaction.
Comments18 pages, 2 figures
