GENERATION

RAG综述：《A Comprehensive Survey of Retrieval-Augmented Generation (RAG)》

来源于《A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions》

Survey Comprehensive RAG GENERATION Augmented

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Comprehensive Overview of Random Number Generation Algorithms

Comprehensive Overview of Random Number Generation Algorithms: History, Mechanics, and Trade-offsManual Methods (Pre-20t

random Overview Comprehensive Algorithms GENERATION

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Retrieval Augmented Generation (RAG): A Comprehensive Visual Walkthrough

GENERATION Augmented retrieval RAG walkthrough

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【论文阅读】Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach

结论：LC性能更好（LCM能力一直在持续提升），而RAG的优势在于低成本。为了兼顾低成本和性能，论文提出了一个trade-off方

论文 GENERATION long Augmented Context

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Rise of Kotlin: The Programming Language for the Next Generation

Rise of Kotlin: The Programming Language for the Next Generation https:hackernoonrise-of-kotlin-the-programming-langu

kotlin Rise programming GENERATION Language

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百度UIE：Unified Structure Generation for Universal Information Extraction paper详细解读和相关资料

Promptlearning系列之信息抽取模型UIE：https:mp.weixin.qqs0lNUlUF_x95mED5B9iBpGg作者解读：https:www.bilibili

相关资料 详细 Structure GENERATION UIE

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论文笔记 StructCoder: Structure-Aware Transformer for Code Generation

目录简单介绍摘要相关模型结构的比较本文的贡献 StructCoder模型与设计符号表示 Code AST DFG 编码器Encoder 输入编码Input emdedding 具有结构感知力的自注意力机制

笔记论文 StructCoder Structure GENERATION

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UIE:Unified Structure Generation for Universal Information Extraction

论文：https:arxivpdf2203.12277.pdf 作者采用生成式text to structure结构统一了信息抽取的四个任务，并且在13个数据集上采用有监督、低资源和少样本

Structure Unified UIE GENERATION Extraction

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《论文阅读》PAL: Persona-Augmented Emotional Support Conversation Generation

《论文阅读》PAL: Persona-Augmented Emotional Support Conversation Generation 前言简介思路出发点相关知识 coefficient of determination 任务定

论文 Persona PAL Augmented GENERATION

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关于conversation generation的论文笔记

对话模型此前的研究大致有三个方向：基于规则、基于信息检索、基于机器翻译。基于规则的对话系统，顾名思义，依赖于人们周密设计的规则，对话内容限制在特定领域下

笔记论文 Conversation GENERATION

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【EMNLP2019】Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs

p4 in 2019129 论文名称：Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs

Aware Conversation Knowledge GENERATION Augmented

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Grounded Conversation Generation as Guided Traverses in Commonsense Knowledge Graphs

论文目的用常识性知识图显式地建模会话流论文方法通过将对话与概念空间联系起来，ConceptFlow将潜在的对话流表示为沿着常识关系在概念空间中遍历。任务描述user输入话语X（有m个单词

GENERATION Guided Grounded Conversation Knowledge

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EXPLORING THE POTENTIAL OF LARGE LANGUAGE MODELS IN GRAPH GENERATION

本文是LLM系列文章，针对《EXPLORING THE POTENTIAL OF LARGE LANGUAGE MODELS IN GRAPH GENERATION》的翻译。探讨大型语言模型在图生成中的潜力摘要1 引言2 LLM4Gr

Large potential EXPLORING Language GENERATION

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