Towards Trustworthy Retrieval Augmented Generation for Large Language
  Models: A Survey
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.
67ad7da995ff670869168209
Bo Ni
67ad7da995ff67086916820a
Zheyuan Liu
67ad7da995ff67086916820b
Leyao Wang
67ad7da995ff67086916820c
Yongjia Lei
67ad7da995ff67086916820d
Yuying Zhao
67ad7da995ff67086916820e
Xueqi Cheng
67ad7da995ff67086916820f
Qingkai Zeng
67ad7da995ff670869168210
Luna Dong
67ad7da995ff670869168211
Yinglong Xia
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Krishnaram Kenthapadi
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Ryan Rossi
67ad7da995ff670869168214
Franck Dernoncourt
claimed_verified
2025-02-13T08:21:27.740Z
62c5947524171688a9feb992
/avatars/5a151713b9eae8dc566f5957acee3475.svg
Franck Dernoncourt
user
Franck-Dernoncourt
67ad7da995ff670869168215
Md Mehrab Tanjim
67ad7da995ff670869168216
Nesreen Ahmed
67ad7da995ff670869168217
Xiaorui Liu
67ad7da995ff670869168218
Wenqi Fan
67ad7da995ff670869168219
Erik Blasch
67ad7da995ff67086916821a
Yu Wang
67ad7da995ff67086916821b
Meng Jiang
67ad7da995ff67086916821c
Tyler Derr
Towards Trustworthy Retrieval Augmented Generation for Large Language
  Models: A Survey
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.
A roadmap for developing trustworthy Retrieval-Augmented Generation (RAG) systems by addressing challenges across reliability, privacy, safety, fairness, explainability, and accountability perspectives.
Retrieval-Augmented Generation RAG Artificial Intelligence-Generated Content AIGC context retrieval content generation hallucinations robustness issues privacy concerns adversarial attacks accountability issues