以评估阶段的结论为指导,对原有的认知过程进行有效调节。评估阶段的结论可以分为三类:(a)知识不足;(b)知识冲突;(c)错误推理。对应的解决方案如下: 知识不足:给定问题q,检索到的文档Dq和答案y,利用‘评估者-批评者’LLM的内省能力来推断仍缺乏哪些外部知识。 知识冲突:可以分为两种情况:(a)仅内部知识能回答问...
A Survey on RAG Meets LLMs: Towards Retrieval-Augmented Large Language Models - 走向增强检索的大型语言模型 摘要 作为人工智能中最先进的技术之一,检索增强生成(RAG)技术可以提供可靠且最新的外部知识,为众多任务提供巨大的便利。特别是在人工智能生成内容(AIGC)的时代,RAG在提供额外知识方面的强大能力使得检索增...
[IR] Knowledge-Augmented Large Language Models for Personalized Contextual Query Suggestion O网页链接 提出一种知识增强大语言模型,以实现个性化上下文查询建议的新方法。该方法利用搜索日志构建每个用户的实体中心知识存储,提取搜索历史中出现的实体,并给予不同权重。在当前搜索上下文中检索与用户知识更好匹配的实体后,...
Title: RALLE: A Framework for Developing and Evaluating Retrieval-Augmented Large Language Models (R-LLMs)url:https://arxiv.org/abs/2308.10633v1论文简要 :本文提出了一个名为RALLE的开源框架,用于开发和评估检索增强的大型语言模型(R-LLMs),以提高事实问答的
We tested several large language models enhanced with retrieval-augmented generation (RAG-LLM) to assess their performance on this task.Methods:We extracted eligibility criteria of 184 oncology trials with FDA approval notifications between 8 January 2020 and 18 January 2024, as well as information ...
The articles in this series discuss the knowledge retrieval models that LLMs use to generate their responses. By default, 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 ...
Chain of Thought Prompting Elicits Reasoning in Large Language Models, Arxiv 2022 本文来自于Google。该篇文章在标准的prompt的基础上,提出为每个example加入对问题答案的解释(explanation),目标在于让模型模仿给出的example的思维过程并为当前query生成...
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...
arXiv preprint. 2024.BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models 本文提出一个用于长上下文建模检索增强的新方法,称为 Landmark Embedding。该方法具有三重技术贡献。第一,本文引入了无分块架构。第二,本文提出了位置感知目标函数。第三,本...
Retrieval-Augmented Generation for Large Language Models: A Survey Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, Qianyu Guo, Meng Wang, Haofen Wang 2023 Retrieval-Augmented Generation for Knowledge-Intensive N...