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
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
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
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
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
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
Explore the diverse use cases of RAG across various fields, from enhancing customer support to analyzing financial markets. Retrieval-Augmented Generation (RAG) is a game-changing technology that combines artificial intelligence with information retrieval and language generation capabilities, enabling AI systems to provide users with accurate, data-driven responses. This approach is particularly effective in customer support, healthcare, … 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
A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future DirectionsThis article delves into the development of Retrieval-Augmented Generation (RAG), from basic concepts to the latest technologies. RAG effectively enhances output accuracy by combining retrieval and generation models, overcoming the limitations of LLMs. The study details the architecture of RAG, demonstrating how retrieval … Read more
AI is a knowledge worker’s excavator, significantly enhancing productivity in teaching and research. #AI Teacher Wang Jue’s AIGC Education Application Articles Collection ———————————————— In the era of large models, the threshold for many professional tasks has been greatly reduced! This means that many tasks no longer require professional skills or extensive training. As long as … Read more