Augmented Language Models: a Surveyarxiv.org/abs/2302.07842 作者:Meta AI的团队 论文发表的目的是回顾和总结那些通过增加推理能力和使用工具能力来增强的语言模型(LMs)的研究工作。 研究动机 LLMs取得的巨大进展不再多说,尽管如此,LLMs仍面临重大限制,如生成非事实性但看似合理的预测(幻觉)、在算术等任务上...
This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools. The former is defined as decomposing a potentially complex task into simpler subtasks while the latter consists in calling external modules such as a code interpreter. LMs ...
Yann LeCun等发布:Augmented Language Models: a Survey 发布于 2023-02-16 15:20・IP 属地广东 喜欢 分享收藏 举报 写下你的评论... 暂无评论登录知乎,您可以享受以下权益: 更懂你的优质内容 更专业的大咖答主 更深度的互动交流 更高效的创作环境立即登录/注册 ...
https://arxiv.org/abs/2312.10997 本文原作为论文“Retrieval-Augmented Generation for Large Language Models: A Survey”,作者为Yunfan Gao、Yun Xiong、Xinyu Gao 、Kangxiang Jia、Jinliu Pan、Yuxi Bi、Yi Dai、Jiawei Sun、Qianyu Guo、Meng Wang、Haofen Wang。该论文全面审视了大型语言模型中的RAG(检索增...
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...
LLM之RAG:《Retrieval-Augmented Generation for Large Language Models: A Survey大型语言模型的检索增强生成研究综述》翻译与解读 导读:这篇论文主要围绕信息检索增强生成(Retrieval Augmented Generation,简称RAG)技术进行概述和分析。 背景痛点: >> 大语言模型(LLM)在处理知识密集型任务和回答离线知识更丰富的问题时面临...
RAG新综述 | A Survey on Retrieval-Augmented Text Generation for Large Language ModelsPaper: 链接#rag #检索增强生成 #RAG搜索增强内容生成 #大模型 发布于 2024-04-18 13:49・IP 属地江苏 赞同22 分享收藏 写下你的评论... 还没有评论,发表第一个评论吧...
231 -- 10:36 App Paper3/3: Cognitive Architectures for Language Agents 144 -- 24:23 App Resource Scheduling in Edge Computing: A Survey 106 -- 18:08 App AUGER: Automatically Generating Review Comments with Pre-training Models浏览方式(推荐使用) 哔哩哔哩 你感兴趣的视频都在B站 打开信息...
Large Language Models (LLMs) showcase remarkable abilities, yet they struggle with limitations such as hallucinations, outdated knowledge, opacity, and inexplicable reasoning. To address these challenges, Retrieval-Augmented Generation (RAG) has proven to be a viable solution, leveraging external database...
Retrieval-Augmented Generation for Large Language Models: A Surveyarxiv.org/abs/2312.10997 Introduction 传统上,神经网络通过微调模型来参数化知识,从而适应特定领域或专有信息。虽然这项技术产生了显著的结果,但它需要大量的计算资源,成本高昂,并且需要专门的技术专业知识,使其不太适应不断变化的信息环境。参数...