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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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