title: Towards Trustworthy Retrieval Augmented Generation for Large Language
    Models: A Survey
summary: 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.
paper_authors._id: 67ad7da995ff670869168209
paper_authors.name: Bo Ni
paper_authors._id: 67ad7da995ff67086916820a
paper_authors.name: Zheyuan Liu
paper_authors._id: 67ad7da995ff67086916820b
paper_authors.name: Leyao Wang
paper_authors._id: 67ad7da995ff67086916820c
paper_authors.name: Yongjia Lei
paper_authors._id: 67ad7da995ff67086916820d
paper_authors.name: Yuying Zhao
paper_authors._id: 67ad7da995ff67086916820e
paper_authors.name: Xueqi Cheng
paper_authors._id: 67ad7da995ff67086916820f
paper_authors.name: Qingkai Zeng
paper_authors._id: 67ad7da995ff670869168210
paper_authors.name: Luna Dong
paper_authors._id: 67ad7da995ff670869168211
paper_authors.name: Yinglong Xia
paper_authors._id: 67ad7da995ff670869168212
paper_authors.name: Krishnaram Kenthapadi
paper_authors._id: 67ad7da995ff670869168213
paper_authors.name: Ryan Rossi
paper_authors._id: 67ad7da995ff670869168214
paper_authors.name: Franck Dernoncourt
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T08:21:27.740Z
paper_authors._id: 62c5947524171688a9feb992
paper_authors.avatarUrl: /avatars/5a151713b9eae8dc566f5957acee3475.svg
paper_authors.fullname: Franck Dernoncourt
paper_authors.type: user
paper_authors.user: Franck-Dernoncourt
paper_authors._id: 67ad7da995ff670869168215
paper_authors.name: Md Mehrab Tanjim
paper_authors._id: 67ad7da995ff670869168216
paper_authors.name: Nesreen Ahmed
paper_authors._id: 67ad7da995ff670869168217
paper_authors.name: Xiaorui Liu
paper_authors._id: 67ad7da995ff670869168218
paper_authors.name: Wenqi Fan
paper_authors._id: 67ad7da995ff670869168219
paper_authors.name: Erik Blasch
paper_authors._id: 67ad7da995ff67086916821a
paper_authors.name: Yu Wang
paper_authors._id: 67ad7da995ff67086916821b
paper_authors.name: Meng Jiang
paper_authors._id: 67ad7da995ff67086916821c
paper_authors.name: Tyler Derr
paper_title: Towards Trustworthy Retrieval Augmented Generation for Large Language
    Models: A Survey
paper_summary: 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.
paper_ai_keywords: Retrieval-Augmented Generation (RAG) Artificial Intelligence-Generated Content (AIGC) Context retrieval Content generation Hallucinations Robustness Privacy concerns Adversarial attacks Accountability Trustworthiness Reliability Safety Fairness Explainability