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

arXiv: https://arxiv.org/abs/2411.02353 · HTML: https://mlsanity.com/paper/2411.02353/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Nov 2024.

## Built on by (most cited first, 7 of 7)

- [Understanding, Protecting, and Augmenting Human Cognition with Generative AI: A Synthesis of the CHI 2025 Tools for Thought Workshop](https://mlsanity.com/paper/2508.21036/) (2025)
- [From Deferral to Learning: Online In-Context Knowledge Distillation for LLM Cascades](https://mlsanity.com/paper/2509.22984/) (2025)
- ["Shall We Dig Deeper?": Designing and Evaluating Strategies for LLM Agents to Advance Knowledge Co-Construction in Asynchronous Online Discussions](https://mlsanity.com/paper/2509.23327/) (2025)
- [Botender: Supporting Communities in Collaboratively Designing AI Agents through Case-Based Provocations](https://mlsanity.com/paper/2509.25492/) (2025)
- [SoulSeek: Exploring the Use of Social Cues in LLM-based Information Seeking](https://mlsanity.com/paper/2601.01094/) (2026)
- [GroupGPT: A Token-efficient and Privacy-preserving Agentic Framework for Multi-User Chat Assistant](https://mlsanity.com/paper/2603.01059/) (2026)
- [PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams](https://mlsanity.com/paper/2606.07454/) (2026)

## Abstract (arXiv)

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 textscPaperPing, 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.