FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG

• URL: http://arxiv.org/abs/2410.10293v1
• Date: Mon, 14 Oct 2024 08:47:21 GMT
• Title: FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG
• Authors: Xinping Zhao, Yan Zhong, Zetian Sun, Xinshuo Hu, Zhenyu Liu, Dongfang Li, Baotian Hu, Min Zhang,
• Abstract summary: Retrieval-Augmented Generation (RAG) prevails in Large Language Models. We propose a progressive retrieval paradigm with coarse-to-fine granularity for RAG.
• Score: 22.4664221738095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) prevails in Large Language Models. It mainly consists of retrieval and generation. The retrieval modules (a.k.a. retrievers) aim to find useful information used to facilitate generation modules (a.k.a. generators). As such, generators' performance largely depends on the effectiveness and efficiency of retrievers. However, the retrieval paradigm that we design and use remains flat, which treats the retrieval procedures as a one-off deal with constant granularity. Despite effectiveness, we argue that they suffer from two limitations: (1) flat retrieval exerts a significant burden on one retriever; (2) constant granularity limits the ceiling of retrieval performance. In this work, we propose a progressive retrieval paradigm with coarse-to-fine granularity for RAG, termed FunnelRAG, so as to balance effectiveness and efficiency. Specifically, FunnelRAG establishes a progressive retrieval pipeline by collaborating coarse-to-fine granularity, large-to-small quantity, and low-to-high capacity, which can relieve the burden on one retriever and also promote the ceiling of retrieval performance. Extensive experiments manifest that FunnelRAG achieves comparable retrieval performance while the time overhead is reduced by nearly 40 percent.

Related papers

• Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers [6.773411876899064]
  inference-free sparse models lag far behind in terms of search relevance when compared to both sparse and dense siamese models. We propose two different approaches for performance improvement. First, we introduce the IDF-aware FLOPS loss, which introduces Inverted Document Frequency (IDF) to the sparsification of representations. We find that it mitigates the negative impact of the FLOPS regularization on search relevance, allowing the model to achieve a better balance between accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-11-07T03:46:43Z)
• Exploring Demonstration Retrievers in RAG for Coding Tasks: Yeas and Nays! [6.34946724864899]
  This paper systematically evaluates the efficiency-effectiveness trade-off of retrievers across three coding tasks. We show that while BM25 excels in effectiveness, it suffers in efficiency as the knowledge base grows beyond 1000 entries. In large-scale retrieval, efficiency differences become more pronounced, with approximate dense retrievers offering the greatest gains.
  arXiv  Detail & Related papers  (2024-10-12T22:31:01Z)
• Intrinsic Evaluation of RAG Systems for Deep-Logic Questions [2.869056892890114]
  We introduce the Overall Performance Index (OPI), an intrinsic metric to evaluate retrieval-augmented generation (RAG) mechanisms for applications involving deep-logic queries. OPI is computed as the harmonic mean of two key metrics: the Logical-Relation Correctness Ratio and the average of BERT embedding similarity scores between ground-truth and generated answers.
  arXiv  Detail & Related papers  (2024-10-03T19:25:05Z)
• Preference-Guided Refactored Tuning for Retrieval Augmented Code Generation [10.736876118242384]
  We propose RRG (Retrieve, Refactor, Generate), a novel framework for effective and efficient code generation. This framework introduces a code-auger module between the retriever and the generator to bridge them. RRG achieved significant performance improvements, with increases of up to 28% on EM, 13% on BLEU, and 6.8% on CodeBLEU.
  arXiv  Detail & Related papers  (2024-09-24T09:15:37Z)
• EfficientRAG: Efficient Retriever for Multi-Hop Question Answering [52.64500643247252]
  We introduce EfficientRAG, an efficient retriever for multi-hop question answering. Experimental results demonstrate that EfficientRAG surpasses existing RAG methods on three open-domain multi-hop question-answering datasets.
  arXiv  Detail & Related papers  (2024-08-08T06:57:49Z)
• Unified Active Retrieval for Retrieval Augmented Generation [69.63003043712696]
  In Retrieval-Augmented Generation (RAG), retrieval is not always helpful and applying it to every instruction is sub-optimal. Existing active retrieval methods face two challenges: 1. They usually rely on a single criterion, which struggles with handling various types of instructions. They depend on specialized and highly differentiated procedures, and thus combining them makes the RAG system more complicated.
  arXiv  Detail & Related papers  (2024-06-18T12:09:02Z)
• Distillation Enhanced Generative Retrieval [96.69326099136289]
  Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. In this work, we identify a viable direction to further enhance generative retrieval via distillation and propose a feasible framework, named DGR. We conduct experiments on four public datasets, and the results indicate that DGR achieves state-of-the-art performance among the generative retrieval methods.
  arXiv  Detail & Related papers  (2024-02-16T15:48:24Z)
• Corrective Retrieval Augmented Generation [36.04062963574603]
  Retrieval-augmented generation (RAG) relies heavily on relevance of retrieved documents, raising concerns about how the model behaves if retrieval goes wrong. We propose the Corrective Retrieval Augmented Generation (CRAG) to improve the robustness of generation. CRAG is plug-and-play and can be seamlessly coupled with various RAG-based approaches.
  arXiv  Detail & Related papers  (2024-01-29T04:36:39Z)
• ReFIT: Relevance Feedback from a Reranker during Inference [109.33278799999582]
  Retrieve-and-rerank is a prevalent framework in neural information retrieval. We propose to leverage the reranker to improve recall by making it provide relevance feedback to the retriever at inference time.
  arXiv  Detail & Related papers  (2023-05-19T15:30:33Z)
• LaPraDoR: Unsupervised Pretrained Dense Retriever for Zero-Shot Text Retrieval [55.097573036580066]
  Experimental results show that LaPraDoR achieves state-of-the-art performance compared with supervised dense retrieval models. Compared to re-ranking, our lexicon-enhanced approach can be run in milliseconds (22.5x faster) while achieving superior performance.
  arXiv  Detail & Related papers  (2022-03-11T18:53:12Z)
• Adversarial Retriever-Ranker for dense text retrieval [51.87158529880056]
  We present Adversarial Retriever-Ranker (AR2), which consists of a dual-encoder retriever plus a cross-encoder ranker. AR2 consistently and significantly outperforms existing dense retriever methods. This includes the improvements on Natural Questions R@5 to 77.9%(+2.1%), TriviaQA R@5 to 78.2%(+1.4), and MS-MARCO MRR@10 to 39.5%(+1.3%)
  arXiv  Detail & Related papers  (2021-10-07T16:41:15Z)

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.