Optimizing End-to-End Retrieval-Augmented Generation

0:00 Zhao shixuan
1:07 Xue chengyao
3:25 Wang qifeng

This paper proposes an End-to-End (E2E) Retrieval-Augmented Generation (RAG) system designed to enhance the performance of natural language processing (NLP) tasks that require both retrieval and generation of knowledge. Traditional generative models often struggle with incorporating external knowledge, especially in dynamic, domain-specific tasks. To address this, we integrate retrieval-based models with generative models, allowing for realtime retrieval of relevant information during inference, thus enabling the system to generate more accurate, up-to-date, and contextually appropriate responses. Our approach employs state-of-the-art models, including the BGE (Bidirectional Generative Embedding) model for retrieval and the Llama-2 model for generation. We use a joint training framework that optimizes both modules simultaneously, improving the overall coherence and relevance of generated outputs. We evaluate the system on several benchmark tasks, including opendomain question answering and information retrieval, showing that our E2E RAG system outperforms traditional methods in both accuracy and knowledge attribution. The experimental results demonstrate significant improvements in dynamic retrieval, text generation, and knowledge integration. Our work contributes to advancing the state-of-the-art in RAG systems and lays the foundation for future research in improving system efficiency, scaling up to larger models, and exploring multi-modal applications.