RemoteRAG: A Privacy-Preserving LLM Cloud RAG Service

Retrieval-augmented generation (RAG) improves the service quality of large language models by retrieving relevant documents from credible literature and integrating them into the context of the user query. Recently, the rise of the cloud RAG service has made it possible for users to query relevant documents conveniently. However, directly sending queries to the cloud brings potential privacy leakage. In this paper, we are the first to formally define the privacy-preserving cloud RAG service to protect the user query and propose RemoteRAG as a solution regarding privacy, efficiency, and accuracy. For privacy, we introduce $(n,\epsilon)$-DistanceDP to characterize privacy leakage of the user query and the leakage inferred from relevant documents. For efficiency, we limit the search range from the total documents to a small number of selected documents related to a perturbed embedding generated from $(n,\epsilon)$-DistanceDP, so that computation and communication costs required for privacy protection significantly decrease. For accuracy, we ensure that the small range includes target documents related to the user query with detailed theoretical analysis. Experimental results also demonstrate that RemoteRAG can resist existing embedding inversion attack methods while achieving no loss in retrieval under various settings. Moreover, RemoteRAG is efficient, incurring only $0.67$ seconds and $46.66$KB of data transmission ($2.72$ hours and $1.43$ GB with the non-optimized privacy-preserving scheme) when retrieving from a total of $10^6$ documents.

Generative Organizational Behavior Simulation using Large Language Model based Autonomous Agents: A Holacracy Perspective

In this paper, we present the technical details and periodic findings of our project, CareerAgent, which aims to build a generative simulation framework for a Holacracy organization using Large Language Model-based Autonomous Agents. Specifically, the simulation framework includes three phases: construction, execution, and evaluation, and it incorporates basic characteristics of individuals, organizations, tasks, and meetings. Through our simulation, we obtained several interesting findings. At the organizational level, an increase in the average values of management competence and functional competence can reduce overall members' stress levels, but it negatively impacts deeper organizational performance measures such as average task completion. At the individual level, both competences can improve members' work performance. From the analysis of social networks, we found that highly competent members selectively participate in certain tasks and take on more responsibilities. Over time, small sub-communities form around these highly competent members within the holacracy. These findings contribute theoretically to the study of organizational science and provide practical insights for managers to understand the organization dynamics.