Related papers: Toward Optimal Search and Retrieval for RAG

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:
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

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.