Despite the impressive capabilities of large language models (LLMs) across
diverse applications, they still suffer from trustworthiness issues, such as
hallucinations and misalignments. Retrieval-augmented language models (RAG)
have been proposed to enhance the credibility of generations by grounding
external knowledge, but the theoretical understandings of their generation
risks remains unexplored. In this paper, we answer: 1) whether RAG can indeed
lead to low generation risks, 2) how to provide provable guarantees on the
generation risks of RAG and vanilla LLMs, and 3) what sufficient conditions
enable RAG models to reduce generation risks. We propose C-RAG, the first
framework to certify generation risks for RAG models. Specifically, we provide
conformal risk analysis for RAG models and certify an upper confidence bound of
generation risks, which we refer to as conformal generation risk. We also
provide theoretical guarantees on conformal generation risks for general
bounded risk functions under test distribution shifts. We prove that RAG
achieves a lower conformal generation risk than that of a single LLM when the
quality of the retrieval model and transformer is non-trivial. Our intensive
empirical results demonstrate the soundness and tightness of our conformal
generation risk guarantees across four widely-used NLP datasets on four
state-of-the-art retrieval models.

本研究提出了 C-RAG 框架，旨在为 RAG 模型证明世代风险。具体地，我们为 RAG 模型提供了符合风险分析，并认证了世代风险的上界置信度称为符合世代风险。我们还对测试分布转移下的一般有界风险函数的符合世代风险提供了理论保证。当检索模型和变换器的质量非平凡时，我们证明了 RAG 模型实现了比单个 LLM 更低的符合世代风险。通过对四个广泛使用的自然语言处理数据集在四个最先进的检索模型上进行的强化实证结果表明了我们符合尾世代风险保证的可靠性和紧密性。