Toward Optimal Search and Retrieval for RAG

• URL: http://arxiv.org/abs/2411.07396v1
• Date: Mon, 11 Nov 2024 22:06:51 GMT
• Title: Toward Optimal Search and Retrieval for RAG
• Authors: Alexandria Leto, Cecilia Aguerrebere, Ishwar Bhati, Ted Willke, Mariano Tepper, Vy Ai Vo,
• Abstract summary: Retrieval-augmented generation (RAG) is a promising method for addressing some of the memory-related challenges associated with Large Language Models (LLMs) Here, we work towards the goal of understanding how retrievers can be optimized for RAG pipelines for common tasks such as Question Answering (QA)
• Score: 39.69494982983534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-augmented generation (RAG) is a promising method for addressing some of the memory-related challenges associated with Large Language Models (LLMs). Two separate systems form the RAG pipeline, the retriever and the reader, and the impact of each on downstream task performance is not well-understood. Here, we work towards the goal of understanding how retrievers can be optimized for RAG pipelines for common tasks such as Question Answering (QA). We conduct experiments focused on the relationship between retrieval and RAG performance on QA and attributed QA and unveil a number of insights useful to practitioners developing high-performance RAG pipelines. For example, lowering search accuracy has minor implications for RAG performance while potentially increasing retrieval speed and memory efficiency.

Related papers

• LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers [24.01783076521377]
  Retrieval-Augmented Generation (RAG) is a crucial method for mitigating hallucinations in Large Language Models (LLMs) Existing RAG methods typically employ query rewriting to clarify the user intent and manage multi-hop logic, while using hybrid retrieval to expand search scope. We introduce a high-level searcher that decomposes complex queries into atomic queries, independent of any retriever-specific optimizations. To harness the strengths of sparse retrievers for precise keyword retrieval, we have developed a new sparse searcher that employs Lucene syntax to enhance retrieval accuracy.
  arXiv  Detail & Related papers  (2025-02-25T12:09:16Z)
• Is Relevance Propagated from Retriever to Generator in RAG? [21.82171240511567]
  RAG is a framework for incorporating external knowledge, usually in the form of a set of documents retrieved from a collection. We empirically investigate whether a RAG context comprised of topically relevant documents leads to improved downstream performance.
  arXiv  Detail & Related papers  (2025-02-20T20:21:46Z)
• Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning [51.54046200512198]
  Retrieval-augmented generation (RAG) is extensively utilized to incorporate external, current knowledge into large language models. A standard RAG pipeline may comprise several components, such as query rewriting, document retrieval, document filtering, and answer generation. To overcome these challenges, we propose treating the RAG pipeline as a multi-agent cooperative task, with each component regarded as an RL agent.
  arXiv  Detail & Related papers  (2025-01-25T14:24:50Z)
• 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. 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)
• Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks [11.053340674721005]
  Retrieval-augmented generation (RAG) has gained traction as a powerful approach for enhancing language models by integrating external knowledge sources. This paper proposes an alternative paradigm, cache-augmented generation (CAG) that bypasses real-time retrieval.
  arXiv  Detail & Related papers  (2024-12-20T06:58:32Z)
• Unanswerability Evaluation for Retrieval Augmented Generation [74.3022365715597]
  UAEval4RAG is a framework designed to evaluate whether RAG systems can handle unanswerable queries effectively. We define a taxonomy with six unanswerable categories, and UAEval4RAG automatically synthesizes diverse and challenging queries.
  arXiv  Detail & Related papers  (2024-12-16T19:11:55Z)
• FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG [22.4664221738095]
  Retrieval-Augmented Generation (RAG) prevails in Large Language Models. We propose a progressive retrieval paradigm with coarse-to-fine granularity for RAG.
  arXiv  Detail & Related papers  (2024-10-14T08:47:21Z)
• Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation [72.70046559930555]
  We propose a generic RAG approach called Adaptive Note-Enhanced RAG (Adaptive-Note) for complex QA tasks. Specifically, Adaptive-Note introduces an overarching view of knowledge growth, iteratively gathering new information in the form of notes. In addition, we employ an adaptive, note-based stop-exploration strategy to decide "what to retrieve and when to stop" to encourage sufficient knowledge exploration.
  arXiv  Detail & Related papers  (2024-10-11T14:03:29Z)
• Enhancing Retrieval in QA Systems with Derived Feature Association [0.0]
  Retrieval augmented generation (RAG) has become the standard in long context question answering (QA) systems. We propose a novel extension to RAG systems, which we call Retrieval from AI Derived Documents (RAIDD)
  arXiv  Detail & Related papers  (2024-10-02T05:24:49Z)
• 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)
• Retrieve, Summarize, Plan: Advancing Multi-hop Question Answering with an Iterative Approach [6.549143816134531]
  We propose a novel iterative RAG method called ReSP, equipped with a dual-function summarizer. Experimental results on the multi-hop question-answering HotpotQA and 2WikiMultihopQA demonstrate that our method significantly outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2024-07-18T02:19:00Z)
• Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers [0.0]
  Retrieval-Augmented Generation (RAG) is a prevalent approach to infuse a private knowledge base of documents with Large Language Models (LLM) to build Generative Q&A (Question-Answering) systems. We propose the 'Blended RAG' method of leveraging semantic search techniques, such as Vector indexes and Sparse indexes, blended with hybrid query strategies. Our study achieves better retrieval results and sets new benchmarks for IR (Information Retrieval) datasets like NQ and TREC-COVID datasets.
  arXiv  Detail & Related papers  (2024-03-22T17:13:46Z)
• RAGGED: Towards Informed Design of Retrieval Augmented Generation Systems [51.171355532527365]
  Retrieval-augmented generation (RAG) can significantly improve the performance of language models (LMs) RAGGED is a framework for analyzing RAG configurations across various document-based question answering tasks.
  arXiv  Detail & Related papers  (2024-03-14T02:26:31Z)
• Retrieval-Augmented Generation for AI-Generated Content: A Survey [38.50754568320154]
  Retrieval-Augmented Generation (RAG) has emerged as a paradigm to address such challenges. RAG introduces the information retrieval process, which enhances the generation process by retrieving relevant objects from available data stores. In this paper, we comprehensively review existing efforts that integrate RAG technique into AIGC scenarios.
  arXiv  Detail & Related papers  (2024-02-29T18:59:01Z)
• CRUD-RAG: A Comprehensive Chinese Benchmark for Retrieval-Augmented Generation of Large Language Models [49.16989035566899]
  Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios.
  arXiv  Detail & Related papers  (2024-01-30T14:25:32Z)
• Generation-Augmented Retrieval for Open-domain Question Answering [134.27768711201202]
  Generation-Augmented Retrieval (GAR) for answering open-domain questions. We show that generating diverse contexts for a query is beneficial as fusing their results consistently yields better retrieval accuracy. GAR achieves state-of-the-art performance on Natural Questions and TriviaQA datasets under the extractive QA setup when equipped with an extractive reader.
  arXiv  Detail & Related papers  (2020-09-17T23:08:01Z)

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.