Trustworthiness in Retrieval-Augmented Generation Systems: A Survey

• URL: http://arxiv.org/abs/2409.10102v1
• Date: Mon, 16 Sep 2024 09:06:44 GMT
• Title: Trustworthiness in Retrieval-Augmented Generation Systems: A Survey
• Authors: Yujia Zhou, Yan Liu, Xiaoxi Li, Jiajie Jin, Hongjin Qian, Zheng Liu, Chaozhuo Li, Zhicheng Dou, Tsung-Yi Ho, Philip S. Yu,
• Abstract summary: Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs) We propose a unified framework that assesses the trustworthiness of RAG systems across six key dimensions: factuality, robustness, fairness, transparency, accountability, and privacy.
• Score: 59.26328612791924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) has quickly grown into a pivotal paradigm in the development of Large Language Models (LLMs). While much of the current research in this field focuses on performance optimization, particularly in terms of accuracy and efficiency, the trustworthiness of RAG systems remains an area still under exploration. From a positive perspective, RAG systems are promising to enhance LLMs by providing them with useful and up-to-date knowledge from vast external databases, thereby mitigating the long-standing problem of hallucination. While from a negative perspective, RAG systems are at the risk of generating undesirable contents if the retrieved information is either inappropriate or poorly utilized. To address these concerns, we propose a unified framework that assesses the trustworthiness of RAG systems across six key dimensions: factuality, robustness, fairness, transparency, accountability, and privacy. Within this framework, we thoroughly review the existing literature on each dimension. Additionally, we create the evaluation benchmark regarding the six dimensions and conduct comprehensive evaluations for a variety of proprietary and open-source models. Finally, we identify the potential challenges for future research based on our investigation results. Through this work, we aim to lay a structured foundation for future investigations and provide practical insights for enhancing the trustworthiness of RAG systems in real-world applications.

Related papers

• Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey [92.36487127683053]
  Retrieval-Augmented Generation (RAG) is an advanced technique designed to address the challenges of Artificial Intelligence-Generated Content (AIGC) RAG provides reliable and up-to-date external knowledge, reduces hallucinations, and ensures relevant context across a wide range of tasks. Despite RAG's success and potential, recent studies have shown that the RAG paradigm also introduces new risks, including privacy concerns, adversarial attacks, and accountability issues.
  arXiv  Detail & Related papers  (2025-02-08T06:50:47Z)
• Unanswerability Evaluation for Retrieval Augmented Generation [74.3022365715597]
  UAEval4RAG is a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively. We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries.
  arXiv  Detail & Related papers  (2024-12-16T19:11:55Z)
• Towards Understanding Retrieval Accuracy and Prompt Quality in RAG Systems [7.457969700747294]
  We conduct an early exploratory study toward a better understanding of the mechanism of RAG systems. We focus on four design factors: retrieval document type, retrieval recall, document selection, and prompt techniques. Our study uncovers how each factor impacts system correctness and confidence, providing valuable insights for developing an accurate and reliable RAG system.
  arXiv  Detail & Related papers  (2024-11-29T04:25:31Z)
• Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective [48.40768048080928]
  Retrieval-Augmented Generation (RAG) systems have shown promise in enhancing the performance of Large Language Models (LLMs) This work aims to provide a systematic study on knowledge checking in RAG systems.
  arXiv  Detail & Related papers  (2024-11-21T20:39:13Z)
• CoFE-RAG: A Comprehensive Full-chain Evaluation Framework for Retrieval-Augmented Generation with Enhanced Data Diversity [23.48167670445722]
  Retrieval-Augmented Generation (RAG) aims to generate more accurate and reliable answers with the help of the retrieved context from external knowledge sources. evaluating these systems remains a crucial research area due to the following issues. We propose a Comprehensive Full-chain Evaluation (CoFE-RAG) framework to facilitate thorough evaluation across the entire RAG pipeline.
  arXiv  Detail & Related papers  (2024-10-16T05:20:32Z)
• Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework [77.45983464131977]
  We focus on how likely it is that a RAG model's prediction is incorrect, resulting in uncontrollable risks in real-world applications. Our research identifies two critical latent factors affecting RAG's confidence in its predictions. We develop a counterfactual prompting framework that induces the models to alter these factors and analyzes the effect on their answers.
  arXiv  Detail & Related papers  (2024-09-24T14:52:14Z)
• RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework [69.4501863547618]
  This paper introduces RAGEval, a framework designed to assess RAG systems across diverse scenarios. With a focus on factual accuracy, we propose three novel metrics Completeness, Hallucination, and Irrelevance. Experimental results show that RAGEval outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.
  arXiv  Detail & Related papers  (2024-08-02T13:35:11Z)
• The Power of Noise: Redefining Retrieval for RAG Systems [19.387105120040157]
  Retrieval-Augmented Generation (RAG) has emerged as a method to extend beyond the pre-trained knowledge of Large Language Models. We focus on the type of passages IR systems within a RAG solution should retrieve.
  arXiv  Detail & Related papers  (2024-01-26T14:14:59Z)
• Retrieval-Augmented Generation for Large Language Models: A Survey [17.82361213043507]
  Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating knowledge from external databases.
  arXiv  Detail & Related papers  (2023-12-18T07:47:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.