Original: https://zhuanlan.zhihu.com/p/682253496 Compiled by: Qingke AI Leave a message in the backend ‘ Exchange ‘, Join the NewBee discussion group 1. Background Introduction RAG (Retrieval Augmented Generation) method refers to a combination of retrieval-based models and generative models to improve the quality and relevance of generated text. This method was proposed by Meta in the … Read more
Recently, while writing articles, I wanted to fill in some gaps left by last year’s RAG (Retrieval-Augmented Generation) and hope to share some tips to help everyone with RAG. As the old saying goes: Building a prototype of a large model is easy, but turning it into a product that can actually be put into … Read more
Original: https://zhuanlan.zhihu.com/p/682253496 Organizer: Qingke AI 1. Background Introduction The RAG (Retrieval Augmented Generation) method refers to a combination of retrieval-based models and generative models to improve the quality and relevance of generated text. This method was proposed by Meta in the 2020 paper “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”[1], allowing language models (LM) to acquire … Read more
This article explains the relevant concepts of RAG, combined with code examples based on the “Building a Personal Knowledge Base with ERNIE SDK + LangChain”. Concept In 2020, the Facebook AI Research (FAIR) team published a paper titled “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks”. This paper first introduced the concept of RAG, which is currently … Read more
Introduction Hello everyone, I am Liu Cong from NLP. As the context length supported by large models continues to increase, a debate has emerged online (many groups are discussing this topic, so I would like to share my thoughts) regarding RAG and Long-Context, which is really unnecessary… The main point is that the two are … Read more
↓Recommended Follow↓ Transformers have fundamentally changed deep learning models since their introduction. Today, we will unveil the core concepts behind Transformers: the attention mechanism, encoder-decoder architecture, multi-head attention, and more. Through Python code snippets, you’ll gain a deeper understanding of its principles. 1. Understanding the Attention Mechanism The attention mechanism is a fascinating concept in … Read more
The common challenge faced by non-Transformer architectures is still to prove how high their ceiling can be. Author: Zhang Jin Editor: Chen Caixian The paper “Attention Is All You Need” published by Google in 2017 has become a bible for artificial intelligence today, and the global AI boom can be directly traced back to the … Read more
1. Overview The overall architecture of the Transformer has been introduced in the first section: Data must go through the following before entering the encoder and decoder: Embedding Layer Positional Encoding Layer The encoder stack consists of several encoders. Each encoder contains: Multi-Head Attention Layer Feed Forward Layer The decoder stack consists of several decoders. … Read more
The Analyst Network of Machine Heart Author:Wang Zijia Editor: H4O This article introduces the Transformer family. Recently, the arms race of large language models has dominated much of the discussions among friends, with many articles exploring what these models can do and their commercial value. However, as a small researcher immersed in the field of … Read more
Hello everyone, my name is Wang Ziyou, a friend of Hugo. My master’s degree at Stanford and my current entrepreneurial direction in China are both related to artificial intelligence. Recently, AI large models represented by ChatGPT have sparked a lot of discussions in China, and the venture capital sector has shown a vibrant scene, which … Read more