Lightning Attention-2 is a new type of linear attention mechanism that aligns the training and inference costs of long sequences with those of a 1K sequence length. The limitations on sequence length in large language models greatly restrict their applications in the field of artificial intelligence, such as multi-turn dialogue, long text understanding, and the … Read more
The attention mechanism is potentially a very useful method. In this issue, let’s understand the principles and methods behind the attention mechanism. The original text is in English from https://blog.heuritech.com/2016/01/20/attention-mechanism/ With the development of deep learning and artificial intelligence, many researchers are interested in the “attention mechanism” in neural networks. This article aims to provide … Read more
Author丨Fat Cat, Yi Zhen Zhihu Column丨Machine Learning Algorithms and Natural Language Processing Address丨https://zhuanlan.zhihu.com/p/82477941 Natural Language Processing (NLP) is generally divided into two categories: Natural Language Understanding (NLU) and Natural Language Generation (NLG). The former extracts or analyzes concise logical information from a piece of text, such as Named Entity Recognition (NER) which identifies keywords in … Read more
Doctoral Innovation Forum Issue Seventy On the morning of March 1, 2024, the seventieth issue of the Doctoral Innovation Forum was held online.PhD student Qin Yubin from Tsinghua University’s School of Integrated Circuits presented an academic report titled “High-Efficiency Attention Model Architecture Design”. The report focuses on the attention-based Transformer model, discussing optimization methods for … Read more
Source: Artificial Intelligence Research This article is approximately 5400 words long and is recommended for a reading time of over 10 minutes. This article will help you understand Transformers from all aspects through a Q&A format. Source: Zhihu Author: Wang Chen, who asks questions @ Zhihu Why summarize Transformers through eighteen questions? There are two … Read more
Source: https://www.zhihu.com/question/362131975/answer/3058958207 Author: Wang Chen, who asks questions @ Zhihu (Authorized) Editor: Jishi Platform Why summarize Transformers through eighteen questions? There are two reasons: First, Transformer is the fourth major feature extractor after MLP, RNN, and CNN, also known as the fourth foundational model; the recently popular chatGPT is also based on Transformer, highlighting its … Read more
Click the above“Beginner Learning Visuals” to selectStar or “Pin” Important content delivered promptly Generation of Attention Reason:《Sequence to Sequence Learning with Neural Networks》 Reason for introducing Attention model: Seq2seq compresses the input sequence into a fixed-size hidden variable, similar to our compressed files. This process is lossy and forces the loss of much information from … Read more
Original Source: Machine Heart Edited by: Big Plate Chicken This article introduces a new research on an approximate attention mechanism, HyperAttention, proposed by institutions such as Yale University and Google Research, which accelerates inference time for ChatGLM2 with a context length of 32k by 50%. Transformers have been successfully applied to various learning tasks in … Read more
In deep learning, especially in the field of natural language processing, the Attention mechanism has become a very important method. Its core idea is to allocate different weights based on the relevance of each element in the input sequence to the current element, thereby achieving dynamic focus on the input sequence. In the Attention mechanism, … Read more
Question: I have looked through various materials and read the original papers, which detail how Q, K, and V are obtained through certain operations to produce output results. However, I have not found anywhere that explains where Q, K, and V come from. Isn’t the input of a layer just a tensor? Why are there … Read more