HyperRAG: Enhancing Quality-Efficiency Tradeoffs in Retrieval-Augmented Generation with Reranker KV-Cache Reuse

• URL: http://arxiv.org/abs/2504.02921v1
• Date: Thu, 03 Apr 2025 17:08:42 GMT
• Title: HyperRAG: Enhancing Quality-Efficiency Tradeoffs in Retrieval-Augmented Generation with Reranker KV-Cache Reuse
• Authors: Yuwei An, Yihua Cheng, Seo Jin Park, Junchen Jiang,
• Abstract summary: Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing the performance of large language models (LLMs)<n>We propose HyperRAG, a system that optimize the trade-off between quality and efficiency in RAG pipelines by leveraging KV-cache reuse for efficient reranker inference.<n>We show that HyperRAG achieves a 2 - 3 throughput improvement with decoder-only rerankers while also delivering higher downstream performance compared with traditional RAG service.
• Score: 7.521340060861743
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing the performance of large language models (LLMs) by integrating external knowledge into the generation process. A key component of RAG pipelines is the reranker, which selects the most relevant documents from a pool of retrieved candidates and significantly improves the quality of the generated responses. While rerankers refine the selection of retrieved documents in RAG pipelines, they introduce computational challenges that hinder high throughput and low latency. To address this problem, we propose HyperRAG, a system that optimizes the trade-off between quality and efficiency in RAG pipelines by leveraging KV-cache reuse for efficient reranker inference. By reusing document-side KV-cache, HyperRAG achieves both high-quality generation and system-level efficiency. To fully realize the benefits of KV-cache reuse, HyperRAG incorporates a range of system-level optimizations designed to enhance efficiency and scalability. Experiments show that HyperRAG achieves a 2 - 3 throughput improvement with decoder-only rerankers while also delivering higher downstream performance compared with traditional RAG service.

Related papers

• Lightweight and Direct Document Relevance Optimization for Generative Information Retrieval [49.669503570350166]
  Generative information retrieval (GenIR) is a promising neural retrieval paradigm that formulates document retrieval as a document identifier (docid) generation task. Existing GenIR models suffer from token-level misalignment, where models trained to predict the next token often fail to capture document-level relevance effectively. We propose direct document relevance optimization (DDRO), which aligns token-level docid generation with document-level relevance estimation through direct optimization via pairwise ranking.
  arXiv  Detail & Related papers  (2025-04-07T15:27:37Z)
• RAGO: Systematic Performance Optimization for Retrieval-Augmented Generation Serving [9.962031642362813]
  Retrieval-augmented generation (RAG) is emerging as a popular approach for reliable LLM serving.<n>RAG is a structured abstraction that captures the wide range of RAG algorithms.<n> RAGO is a system optimization framework for efficient RAG serving.
  arXiv  Detail & Related papers  (2025-03-18T18:58:13Z)
• TeleRAG: Efficient Retrieval-Augmented Generation Inference with Lookahead Retrieval [10.268774281394261]
  Retrieval-augmented generation (RAG) extends large language models (LLMs) with external data sources to enhance factual correctness and domain coverage.<n>Modern RAG pipelines rely on large datastores, leading to system challenges in latency-sensitive deployments.<n>We propose TeleRAG, an efficient inference system that reduces RAG latency with minimal GPU memory requirements.
  arXiv  Detail & Related papers  (2025-02-28T11:32:22Z)
• RoseRAG: Robust Retrieval-augmented Generation with Small-scale LLMs via Margin-aware Preference Optimization [53.63439735067081]
  Large language models (LLMs) have achieved impressive performance but face high computational costs and latency.<n>Retrieval-augmented generation (RAG) helps by integrating external knowledge, but imperfect retrieval can introduce distracting noise that misleads SLMs.<n>We propose RoseRAG, a robust RAG framework for SLMs via Margin-aware Preference Optimization.
  arXiv  Detail & Related papers  (2025-02-16T04:56:53Z)
• Chain-of-Retrieval Augmented Generation [72.06205327186069]
  This paper introduces an approach for training o1-like RAG models that retrieve and reason over relevant information step by step before generating the final answer.<n>Our proposed method, CoRAG, allows the model to dynamically reformulate the query based on the evolving state.
  arXiv  Detail & Related papers  (2025-01-24T09:12:52Z)
• RAG-Reward: Optimizing RAG with Reward Modeling and RLHF [8.911260109659489]
  Retrieval-augmented generation (RAG) enhances Large Language Models (LLMs) with relevant and up-to-date knowledge.<n>The role of reward models in reinforcement learning for optimizing RAG remains underexplored.<n>We introduce textbfRAG-Reward, a framework designed to develop reward models.
  arXiv  Detail & Related papers  (2025-01-22T22:59:19Z)
• Digital Twin-Assisted Data-Driven Optimization for Reliable Edge Caching in Wireless Networks [60.54852710216738]
  We introduce a novel digital twin-assisted optimization framework, called D-REC, to ensure reliable caching in nextG wireless networks. By incorporating reliability modules into a constrained decision process, D-REC can adaptively adjust actions, rewards, and states to comply with advantageous constraints.
  arXiv  Detail & Related papers  (2024-06-29T02:40:28Z)
• Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures [17.517010701323823]
  Super-Resolution (SR) is a time-hallowed image processing problem. We introduce a fully-conversaal Generative Adrial Network (GAN)-based architecture for SR. We show that distinct convolutional features obtained at increasing depths of a GAN generator can be optimally combined by a set of learnable convex weights.
  arXiv  Detail & Related papers  (2024-04-09T13:19:43Z)
• PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design [16.76965926088238]
  PipeRAG is a novel algorithm-system co-design approach to reduce generation latency and enhance generation quality. Our evaluation shows that PipeRAG achieves up to 2.6$times$ speedup in end-to-end generation latency while improving generation quality.
  arXiv  Detail & Related papers  (2024-03-08T21:09:20Z)
• Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach [58.57026686186709]
  We introduce the Convolutional Transformer layer (ConvFormer) and propose a ConvFormer-based Super-Resolution network (CFSR) CFSR inherits the advantages of both convolution-based and transformer-based approaches. Experiments demonstrate that CFSR strikes an optimal balance between computational cost and performance.
  arXiv  Detail & Related papers  (2024-01-11T03:08:00Z)
• Fourier Space Losses for Efficient Perceptual Image Super-Resolution [131.50099891772598]
  We show that it is possible to improve the performance of a recently introduced efficient generator architecture solely with the application of our proposed loss functions. We show that our losses' direct emphasis on the frequencies in Fourier-space significantly boosts the perceptual image quality. The trained generator achieves comparable results with and is 2.4x and 48x faster than state-of-the-art perceptual SR methods RankSRGAN and SRFlow respectively.
  arXiv  Detail & Related papers  (2021-06-01T20:34:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.