C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models

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, …

applications capabilities cs.ai cs.cl cs.ir diverse diverse applications hallucinations knowledge language language models large language large language models llms rag retrieval retrieval-augmented risks