检索增强生成(RAG, Retrieval-Augmented Generation) 最早由Lewis等人于2020引入,将预先训练的检索器与预先训练的seq2seq模型(生成器)相结合,并进行端到端的微调,以更可解释和模块化的方式获取知识。 在大型模型出现之前,RAG主要专注于端到端模型的直接优化。在检索端进行密集检索,例如Karpukhin等人使用基于向量的密集...
论文地址: Retrieval-Augmented Generation for Large Language Models: A Survey | PPT 注: 主要是了解RAG的发展过程(召回率),以及对相关子模块领域的现阶段了解,如果感兴趣,通过索引到论文引用处进一步了解。(提高看相应论文的准确率) 第1章:引言 大型语言模型(LLMs)如GPT系列和LLama系列在自然语言处理方面取得...
a Large Language Model (LLM) only has access to its training data. However, you can be augment the model to include real-time data or private data. This article discusses one of two mechanisms for augmenting a model.
arXiv preprint. 2024.M-RAG: Reinforcing Large Language Model Performance through Retrieval-Augmented Generation with Multiple Partitions 本文提出了检索增强生成的一种多分区范式(称为 M-RAG),其中每个数据库分区作为检索增强生成执行的一个基本单元。基于此范式,本文提出一种创新的框架,该框架利用 LLMs 和多智能...
LLM(大型语言模型,Large Language Model)展现出了其强大的能力,但也面临着幻觉、知识过时以及推理过程不透明、无法追踪等诸多挑战。为了应对这些挑战,RAG(检索增强生成,Retrieval-Augmented Generation)应运而生。RAG可以将LLM内在的知识与外部数据库庞大、动态的资源库协同融合,不仅提高了模型的准确性和可信度,还支持持续...
This study explores the implementation of Retrieval Augmented Large Language Models (LLMs) toward enhancing prospective student engagement during University open days. This work proposes the use of local, 4-bit quantised LLMs such as Microsoft Phi3, Meta's LLaMa3, and Mistral AI's Mistral to ...
Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating kn...
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Development and Evaluation of a Retrieval-Augmented Large Language Model Framework for Ophthalmology. JAMA Ophthalmol. Published online July 18, 2024. doi:10.1001/JAMAOPHTHALMOL.2024.2513 2. Introducing GPTs | OpenAI. Accessed August 7, 2024. https://openai.com/index/introducing-gpts/ 3. Sevgi M...
本文是一篇关于检索增强型生成(Retrieval-Augmented Generation, RAG)在大型语言模型(Large Language Models, LLMs)中应用的综述文章。文章由Yunfan Gao等人撰写,总结了RAG在LLMs时代的发展历程,并探讨了其主要范式、关键技术和未来研究方向。以下是文章的核心内容概述: ...