The rapid development of large language models has led to the widespread
adoption of Retrieval-Augmented Generation (RAG), which integrates external
knowledge to alleviate knowledge bottlenecks and mitigate hallucinations.
However, the existing RAG paradigm inevitably suffers from the impact of flawed
information introduced during the retrieval phrase, thereby diminishing the
reliability and correctness of the generated outcomes. In this paper, we
propose Credibility-aware Generation (CAG), a universally applicable framework
designed to mitigate the impact of flawed information in RAG. At its core, CAG
aims to equip models with the ability to discern and process information based
on its credibility. To this end, we propose an innovative data transformation
framework that generates data based on credibility, thereby effectively
endowing models with the capability of CAG. Furthermore, to accurately evaluate
the models' capabilities of CAG, we construct a comprehensive benchmark
covering three critical real-world scenarios. Experimental results demonstrate
that our model can effectively understand and utilize credibility for
generation, significantly outperform other models with retrieval augmentation,
and exhibit resilience against the disruption caused by noisy documents,
thereby maintaining robust performance. Moreover, our model supports customized
credibility, offering a wide range of potential applications.

本研究提出了一个通用的 CAG 框架，旨在减轻 RAG 模型中引入的错误信息对生成结果的影响。通过创新的数据转换框架生成基于可靠性的数据，使模型具备辨别和处理信息可靠性的能力。实验证明，该模型在生成中能够有效理解和利用可靠性，显著优于其他带有检索增强的模型，并对噪声文档引起的干扰表现出韧性，从而保持稳健的性能。此外，该模型支持定制的可靠性，有广泛的潜在应用。