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  2. LLMs
  • LLMs之RAG:《LightRAG: Simple and Fast Retrieval-Augmented Generation》翻译与解读

    LLMs之RAG:《LightRAG: Simple and Fast Retrieval-Augmented Generation》翻译与解读 导读:这篇论文介绍了一种名为LightRAG的检索
    LightRAG simple LLMs RAG Augmented
    admin 3月前
    32 0
  • LLMs之Multi-Turn Conversation:《LLMs Get Lost In Multi-Turn Conversation》的翻译与解读

    LLMs之Multi-Turn Conversation:《LLMs Get Lost In Multi-Turn Conversation》的翻译与解读 导读:该论文通过一个大规模、可复现的“分
    Multi LLMs Turn Lost Conversation
    admin 3月前
    45 0
  • LLMs之GraphRAG:《From Local to Global: A Graph RAG Approach to Query-Focused Summarization》翻译与解读

    LLMs之GraphRAG:《From Local to Global: A Graph RAG Approach to Query-Focused Summarization》翻译与解读 导读:
    global Graph local LLMs GraphRAG
    admin 4月前
    43 0
  • LLMs之Benchmark之TableBench:《TableBench: A Comprehensive and Complex Benchmark for Table Question Answ

    LLMs之Benchmark之TableBench:《TableBench: A Comprehensive and Complex Benchmark for Table Question Answering一个全面
    TableBench Comprehensive LLMs Benchmark question
    admin 6月前
    116 0
  • Understanding LLMs: A Comprehensive Overview from Training to Inference

    Q: 这篇论文试图解决什么问题?A: 这篇论文试图提供一个全面的概述,从训练到推理,关于大型语言模型(LLMs)的发展。它讨论了L
    Comprehensive LLMs Understanding Inference Training
    admin 6月前
    88 0
  • LLMs之RAGLong-Context:《检索增强生成还是长上下文LLMs?一项综合研究与混合方法Retrieval Augmented Generation or Long-Context LL

    LLMs之RAGLong-Context:《检索增强生成还是长上下文LLMs?一项综合研究与混合方法Retrieval Augmented Generation or Long-Context
    长上 下文 方法 long LLMs
    admin 6月前
    117 0
  • LLMs之RAG:《Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make you

    LLMs之RAG:《Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs us
    retrieval RAG LLMs Augmented Survey
    admin 6月前
    102 0
  • LLMs之PEFT:《Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey》翻译与解读

    LLMs之PEFT:《Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey》翻译与解读 导读:这篇论文是
    Efficient Fine parameter LLMs PEFT
    admin 6月前
    121 0
  • LLMs之Nemotron-4:《Nemotron-4 340B Technical Report》翻译与解读

    LLMs之Nemotron-4:《Nemotron-4 340B Technical Report》翻译与解读 导读:>> 背景痛点:越来越大的语言模型需要大量高
    Nemotron LLMs REPORT Technical
    admin 7月前
    114 0
  • LLMs之Alpaca:《Alpaca: A Strong, Replicable Instruction-Following Model》翻译与解读

    LLMs之Alpaca:《Alpaca: A Strong, Replicable Instruction-Following Model》翻译与解读 导读:2023年3月13日发布Alpaca&
    Strong Alpaca LLMs Model Instruction
    admin 7月前
    98 0
  • LLMs之Guanaco:《QLoRA:Efficient Finetuning of Quantized LLMs》翻译与解读

    LLMs之Guanaco:《QLoRA:Efficient Finetuning of Quantized LLMs》翻译与解读 导读:2023年5月23日华盛顿大学发布Gu
    QLoRA Guanaco LLMs Quantized Finetuning
    admin 7月前
    106 0
  • LLMs之MoE之DeepSeek-V3:《DeepSeek-V3 Technical Report》翻译与解读(DeepSeek-V3的最详细解读)

    LLMs之MoE之DeepSeek-V3:《DeepSeek-V3 Technical Report》翻译与解读(DeepSeek-V3的最详细解读) 导读:这篇论文介绍了DeepSeek-V3大
    详细 DeepSeek MoE LLMs REPORT
    admin 7月前
    151 0
  • LLMs:《BLOOM: A 176B-Parameter Open-Access Multilingual Language Model》翻译与解读

    LLMs:《BLOOM: A 176B-Parameter Open-Access Multilingual Language Model》翻译与解读 导读:BLOOM(BigScience La
    parameter LLMs bloom Open Language
    admin 7月前
    132 0
  • LLMs:《Building LLM applications for production构建用于生产的LLM应用程序》翻译与解读

    LLMs:《Building LLM applications for production构建用于生产的LLM应用程序》翻译与解读 LLMs:构建用于生产的LLM应用程序的挑战与案例经验总结—
    应用程序 building LLMs LLM production
    admin 7月前
    113 0
  • 【模型精调LoRA】LoRA 低秩适应微调的工作原理和代码实现示例 What is LoRA? Low-Rank Adaptation for finetuning LLMs EXPLAINED

    Low-Rank Adaptation for Fine-tuning Introduction Fine-tuning is a common technique used in transfer learning, where a
    示例 工作原理 模型 代码 LLMs
    admin 7月前
    83 0
  • 【论文阅读】LLMs Get Lost In Multi-Turn Conversation:大模型多轮对话迷航现象研究

    【论文阅读】LLMs Get Lost In Multi-Turn Conversation:大模型多轮对话迷航现象研究 基本信息 论文链接:LLMs Get Lost In Multi-Turn Conversation 作者:Phil
    模型 现象 论文 LLMs Multi
    admin 7月前
    67 0
  • LLMs之ChatGPT:《Connecting GitHub to ChatGPT deep research》翻译与解读

    LLMs之ChatGPT:《Connecting GitHub to ChatGPT deep research》翻译与解读 导读:这篇OpenAI帮助文档全面介绍了将GitHub连接到ChatG
    connecting ChatGpt LLMs research deep
    admin 8月前
    72 0
  • LLMs之Agent之Safety:《A Comprehensive Survey in LLM(-Agent) Full Stack Safety:Data, Training and Deploy

    LLMs之Agent之Safety:《A Comprehensive Survey in LLM(-Agent) Full Stack Safety:Data, Training and Depl
    Comprehensive Survey safety LLMs Agent
    admin 8月前
    93 0
  • LLMs之Multi-Turn:《LLMs Get Lost In Multi-Turn Conversation》翻译与解读

    LLMs之Multi-Turn:《LLMs Get Lost In Multi-Turn Conversation》翻译与解读 导读:该论文通过模拟实验揭示了LLM在多轮、不明确对话中存在严重的性
    Multi LLMs Turn Conversation Lost
    admin 8月前
    76 0
  • LLMs:《PaLM: Scaling Language Modeling with Pathways》翻译与解读

    LLMs:《PaLM: Scaling Language Modeling with Pathways》翻译与解读 导读:这项工作介绍了Pathways Language Model&#x
    Scaling PaLM LLMs Pathways Modeling
    admin 8月前
    95 0
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