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
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
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
This article is about 11,000 words long and is recommended to be read in 6 minutes. This article introduces large models + RAG. 1 Introduction Large Language Models (LLMs) have limitations when handling domain-specific or highly specialized queries, such as generating inaccurate information or “hallucinations.” A promising approach to mitigate these limitations is Retrieval-Augmented Generation … Read more
Click the “Blue WeChat Name” below the title to quickly follow In the era of large models, many new terms have emerged, and RAG is one of them. This article from the tech community, “Understanding RAG (Retrieval Augmented Generation) in One Article,” explains what RAG is, its functions, and the associated challenges. Related historical articles … Read more
2023 marks a year of explosive growth for large language models in artificial intelligence, with several concepts and English abbreviations emerging this year, leading to confusion and even bewilderment. LLM: Large Language Model, refers to models designed to understand and generate human language. LLMs are characterized by their massive scale, containing hundreds of billions of … Read more
Source: Algorithm Advancement This article is about 4000 words long and is recommended for an 8-minute read. This article deeply explores the potential connections between Transformer, Recurrent Neural Networks (RNN), and State Space Models (SSM). By exploring the potential connections between seemingly unrelated Large Language Model (LLM) architectures, we may open up new avenues for … Read more
Originally from AI有道 Seven years ago, the paper “Attention is All You Need” introduced the transformer architecture, revolutionizing the entire field of deep learning. Today, all major models are based on the transformer architecture, yet the internal workings of the transformer remain a mystery. Last year, one of the authors of the transformer paper, Llion … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering NLP graduate students, university professors, and corporate researchers. The Vision of the Community is to promote communication and progress between academia, industry, and enthusiasts in the field of natural language processing and machine learning, especially for beginners. Reprinted … Read more
MLNLP community is a well-known machine learning and natural language processing community both domestically and internationally, covering audiences including NLP graduate students, university teachers, and corporate researchers. The community’s vision is to promote communication and progress between the academic and industrial sectors of natural language processing and machine learning, especially for the advancement of beginners. … Read more