Agentic RAG

Can RAG Improve LLM Inference Ability? Recent Research from Renmin University

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Can RAG Improve LLM Inference Ability? Recent Research from Renmin University

MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, with an audience covering NLP graduate students, university teachers, and industry researchers. The Vision of the Community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for the … Read more

Advanced RAG: Enhancing RAG Performance

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Advanced RAG: Enhancing RAG Performance

Author: Luv Bansal Translation: wwl Proofreading: Zhang Yiran This article is approximately 4400 words long and is recommended for a reading time of over 10 minutes. This article discusses various techniques for optimizing different parts of the RAG pipeline and enhancing the overall RAG workflow. Image generated by the author using Dalle-3 provided by Bing … Read more

Overview of AI Large Models and RAG Technology

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Overview of AI Large Models and RAG Technology

Originally published on Data Analysis and Applications 1 Introduction This article provides a comprehensive review of the current state-of-the-art RAG technology, including Naive RAG, Advanced RAG, and Modular RAG paradigms, all within the context of LLMs. The article discusses core technologies in the RAG process such as “retrieval,” “generation,” and “enhancement,” and delves into their … Read more

Top 10 RAG Frameworks on GitHub

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Top 10 RAG Frameworks on GitHub

Source: NewBeeNLP This article is about 3300 words long, and it is recommended to read for 6 minutes. This article introduces Retrieval-Augmented Generation (RAG), a powerful technology that can significantly enhance the performance of large language models. Retrieval-Augmented Generation (RAG) is a powerful technology that can significantly enhance the performance of large language models. The … Read more

12 Pain Points of RAG and Solutions from NVIDIA Architect

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12 Pain Points of RAG and Solutions from NVIDIA Architect

MLNLP community is a well-known machine learning and natural language processing community at home and abroad, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The vision of the community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning at home and abroad, … Read more

Latest Developments in RAG: 16 New Models

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Latest Developments in RAG: 16 New Models

MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP master’s and doctoral students, university teachers, and corporate researchers. The vision of the community is to promote communication and progress between the academic and industrial circles of natural language processing and machine learning, especially for beginners. Reprinted … Read more

Introduction and Practical Guide to RAG for Large Models

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Introduction and Practical Guide to RAG for Large Models

Book Giveaway at the End Since RAG was introduced by Facebook AI Research in 2020, it has rapidly gained popularity. After all, it has truly been a great help, playing a key role in solving the “hallucination” problem of large language models. Today, tech giants like Google, AWS, IBM, Microsoft, and NVIDIA are all supporting … Read more

Beginner Friendly: What Are Large Language Models and RAG?

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Beginner Friendly: What Are Large Language Models and RAG?

What Are Large Language Models (LLM) Large Language Models (LLM), also known as large language models, are a type of artificial intelligence model designed to understand and generate human language. The LLMs we commonly refer to typically contain hundreds of billions (or more) parameters and are trained on massive amounts of text data, allowing them … Read more

Latest Overview of RAG: Review of 15 Classic RAG Frameworks (Part 2)

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Latest Overview of RAG: Review of 15 Classic RAG Frameworks (Part 2)

Source: Deep Graph Learning and Large Model LLM This article is approximately 3500 words long and is recommended for a 9-minute read. It delves into the development of Retrieval-Augmented Generation (RAG), from basic concepts to the latest technologies. 4. Overview of Existing RAG Frameworks Agent-Based RAG A new agent-based Retrieval-Augmented Generation (RAG) framework employs a … Read more

RAG Evaluation Guide: Comprehensive Analysis of LLM Performance Assessment Methods

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RAG Evaluation Guide: Comprehensive Analysis of LLM Performance Assessment Methods

Introduction This article will compare the evaluation methods of RAG from a timeline perspective. These evaluation methods are not limited to the RAG process, and the evaluation methods based on LLM are more applicable across various industries. Common Evaluation Methods for RAG In the previous section, we discussed how to use the ROUGE method to … Read more