Social-RAG: Retrieving from Group Interactions to Socially Ground AI Generation

要旨

AI agents are increasingly tasked with making proactive suggestions in online spaces where groups collaborate, yet risk being unhelpful or even annoying if they fail to match group preferences or behave in socially inappropriate ways. Fortunately, group spaces have a rich history of prior interactions and affordances for social feedback that can support grounding an agent's generations to a group's interests and norms. We present Social-RAG, a workflow for socially grounding agents that retrieves context from prior group interactions, selects relevant social signals, and feeds them into a language model to generate messages in a socially aligned manner. We implement this in \textsc{PaperPing}, a system for posting paper recommendations in group chat, leveraging social signals determined from formative studies with 39 researchers. From a three-month deployment in 18 channels reaching 500+ researchers, we observed PaperPing posted relevant messages in groups without disrupting their existing social practices, fostering group common ground.

著者
Ruotong Wang
University of Washington , Seattle , Washington, United States
Xinyi Zhou
University of Washington, Seattle, Washington, United States
Lin Qiu
University of Washington, Seattle, Washington, United States
Joseph Chee Chang
Allen Institute for AI, Seattle, Washington, United States
Jonathan Bragg
Allen Institute for Artificial Intelligence, Seattle, Washington, United States
Amy X.. Zhang
University of Washington, Seattle, Washington, United States
DOI

10.1145/3706598.3713749

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713749

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Communication and Social Interaction

G403
7 件の発表
2025-04-30 23:10:00
2025-05-01 00:40:00
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