Towards Trustworthy Retrieval Augmented Generation for Large Language
  Models: A Survey
Authors: name: Bo Ni, name: Zheyuan Liu, name: Leyao Wang, name: Yongjia Lei, name: Yuying Zhao, name: Xueqi Cheng, name: Qingkai Zeng, name: Luna Dong, name: Yinglong Xia, name: Krishnaram Kenthapadi, name: Ryan Rossi, name: Franck Dernoncourt, name: Md Mehrab Tanjim, name: Nesreen Ahmed, name: Xiaorui Liu, name: Wenqi Fan, name: Erik Blasch, name: Yu Wang, name: Meng Jiang, name: Tyler Derr

Retrieval-Augmented Generation (RAG) is an advanced technique designed to
address the challenges of Artificial Intelligence-Generated Content (AIGC). By
integrating context retrieval into content generation, RAG provides reliable
and up-to-date external knowledge, reduces hallucinations, and ensures relevant
context across a wide range of tasks. However, despite RAG's success and
potential, recent studies have shown that the RAG paradigm also introduces new
risks, including robustness issues, privacy concerns, adversarial attacks, and
accountability issues. Addressing these risks is critical for future
applications of RAG systems, as they directly impact their trustworthiness.
Although various methods have been developed to improve the trustworthiness of
RAG methods, there is a lack of a unified perspective and framework for
research in this topic. Thus, in this paper, we aim to address this gap by
providing a comprehensive roadmap for developing trustworthy RAG systems. We
place our discussion around five key perspectives: reliability, privacy,
safety, fairness, explainability, and accountability. For each perspective, we
present a general framework and taxonomy, offering a structured approach to
understanding the current challenges, evaluating existing solutions, and
identifying promising future research directions. To encourage broader adoption
and innovation, we also highlight the downstream applications where trustworthy
RAG systems have a significant impact.