Source: Xixiaoyao Technology Author | Richard Since the advent of GPT-4, large models seem to have become increasingly intelligent, possessing an “encyclopedic” knowledge base. But are they really approaching human intelligence? Not quite. Large models still have significant shortcomings in knowledge integration and long-term memory, which are precisely the strengths of the human brain. The … Read more
Author: Raunak Jain March 18, 2024 Translator: Chen Zhiyan Proofreader: zrx This article is approximately 3500 words long and is suggested to be read in 10 minutes. How to build a flow-configurable compound artificial intelligence system using open-source tools. In the previous section, we introduced what a compound artificial intelligence system is, its system components, … Read more
1. What is RAG? Retrieval-Augmented Generation (RAG) refers to optimizing the output of large language models to enable them to reference authoritative knowledge bases outside of the training data sources before generating responses. Large Language Models (LLMs) are trained on vast amounts of data, using billions of parameters to generate raw outputs for tasks like … Read more
Author: Deephub Imba This article is about 3000 words long, and it is recommended to read it in 7 minutes. This article will introduce four strategies for optimizing Retrieval-Augmented Generation (RAG) using private data. In this article, we will introduce four strategies for optimizing Retrieval-Augmented Generation (RAG) using private data, which can enhance the quality … Read more
Introduction RAG has developed to become a consensus for LLM’s service to B-end, however, questions regarding it have never ceased to exist. Simply put: for many Q&A systems represented by individuals and small to medium enterprises, there is indeed no need to use RAG. However, these long-context LLMs have either already addressed or are in … Read more
1 Algorithm Introduction Retrieval Augmented Generation (RAG) has become one of the hottest applications of large language models (LLM). After the recent boom in large models, everyone must have a certain understanding of their capabilities. However, when we apply large models to practical business scenarios, we find that generic foundational models generally cannot meet our … Read more
Source: DeepHub IMBA This article is approximately 4200 words long and is suggested to be read in 5 minutes. This article reveals the main reasons for the failure of ordinary RAGs and provides specific strategies and methods to bring your RAG closer to production stage. Countless companies are attempting to use Retrieval-Augmented Generation (RAG), but … Read more
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 professors, and researchers from enterprises. The vision of the community is to promote communication and progress between the academic and industrial worlds of natural language processing and machine learning, especially for the … Read more
Have you ever been troubled by these questions? Searching through a cluttered computer hard drive for an assignment you saved, clearly remembering the general content, but unable to find it by name because the file name has little to do with the content; or when writing a paper, you remember a piece of literature supporting … Read more
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