OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learning (2503.08398v1)

Abstract: In this paper, we analyze and empirically show that the learned relevance for conventional information retrieval (IR) scenarios may be inconsistent in retrieval-augmented generation (RAG) scenarios. To bridge this gap, we introduce OpenRAG, a RAG framework that is optimized end-to-end by tuning the retriever to capture in-context relevance, enabling adaptation to the diverse and evolving needs. Extensive experiments across a wide range of tasks demonstrate that OpenRAG, by tuning a retriever end-to-end, leads to a consistent improvement of 4.0% over the original retriever, consistently outperforming existing state-of-the-art retrievers by 2.1%. Additionally, our results indicate that for some tasks, an end-to-end tuned 0.2B retriever can achieve improvements that surpass those of RAG-oriented or instruction-tuned 8B LLMs, highlighting the cost-effectiveness of our approach in enhancing RAG systems.

OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learning

The paper "OpenRAG: Optimizing RAG End-to-End via In-Context Retrieval Learning" by Jiawei Zhou and Lei Chen addresses the challenges associated with Retrieval-Augmented Generation (RAG) systems and introduces an innovative approach named Open-Rag. This research primarily focuses on improving RAG frameworks by optimizing the retrieval process to capture in-context relevance specifically tailored for downstream tasks.

The authors begin by highlighting a fundamental issue with conventional Information Retrieval (IR) systems when applied in RAG scenarios. They argue that the relevance learned from typical IR settings does not consistently translate to RAG environments, primarily because the objective of retrieval in RAG systems extends beyond simply finding documents that contain the answer. Instead, the retrieval should facilitate a LLM in generating high-quality outputs with the provided context. The paper proposes Open-Rag, a framework that fine-tunes the retriever end-to-end to meet this nuanced requirement of capturing in-context relevance.

Through comprehensive experiments across multiple tasks, the research shows that Open-Rag outperforms existing retrieval methods. Notably, Open-Rag demonstrates a consistent 4.0% improvement over the original retrievers and outperforms state-of-the-art retrievers by 2.1%. One striking result reported is that a 0.2B model retriever tuned using Open-Rag can sometimes exceed the performance gains provided by much larger RAG-oriented or instruction-tuned LLMs (8B models). This highlights the cost-effectiveness and efficiency of this approach.

The paper also explores the implications of these findings. By training the retriever end-to-end, Open-Rag provides a more economical solution compared to training and deploying larger LLMs while achieving comparable, if not superior, performance enhancements. The approach allows RAG systems to better adapt to a wider array of tasks and contexts than those previously presumed applicable under traditional IR paradigms. As such, it facilitates more dynamic and broadly applicable deployment of LLMs in various real-world settings, making RAG systems both more flexible and resource-efficient.

Looking towards the future, the paper suggests that the principles underlying Open-Rag's optimization strategy could be extended beyond current applications, potentially influencing the development of more sophisticated AI systems. These systems could seamlessly integrate nuanced contextual understanding into large-scale language processing, thereby advancing reliability and versatility in applications such as conversational agents, recommendation systems, and automated content generation.

Overall, this research provides substantive insights and methodologies that contribute to overcoming existing limitations in RAG systems, proposing a promising direction for enhancing LLM capabilities through efficient retrieval optimization.