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

C-RAG: Certified Generation Risks for Retrieval-Augmented Language Models refers to the first framework to certify generation risks for RAG models through conformal risk analysis that provides provable guarantees on generation risks. This theoretical framework addresses trustworthiness issues in large language models by establishing upper confidence bounds for generation risks in retrieval-augmented systems. C-RAG employs conformal prediction theory to quantify uncertainty and provide statistical guarantees about model outputs, ensuring reliability in high-stakes applications. The framework proves that RAG achieves lower conformal generation risk than single LLMs when retrieval model and transformer quality meet non-trivial thresholds. C-RAG provides theoretical foundations for understanding when external knowledge retrieval actually reduces hallucinations and misalignments compared to vanilla language models. The certification process involves bounded risk functions under test distribution shifts, enabling practitioners to deploy RAG systems with quantified reliability measures across diverse natural language processing applications.