原因1：过往研究证明decoder-only泛化化性能更好Google有两篇著名的发表于ICML’22的论文，一个是《Examining Scaling and Transfer of Language Model Architectures for Machine Translation》，另一个是《What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?》，两篇论文...
1. 在LLM发展得早期阶段,encoder-only和encoder-decoder模型更受欢迎。然而,自2021年起,随着游戏规则改变者GPT-3入局,decoder-only经历了显著的增长并逐渐主导LLMs的发展,于此同时在BERT带来的初期爆炸式增长之后,encoder-only模型逐渐开始淡出。 2. encoder-decoder模型仍具有前景,因为这种类型的架构仍在积极探索中,而...
Decoder-Only架构并不是没有信息压缩模型,其信息压缩模型 Q 就是Decoder自身。因此不论是在预训练任务层面以及条件信息的压缩层面相比其他架构GAP都比较小。 但是,Decoder-Only架构的训练任务并不是完全没有GAP,为了使Transformer能够并行训练,大多数Decoder-Only模型预训练时都采用了Teacher Forcing的模式,即训练时,用la...
首先概述几种主要的架构：以BERT为代表的encoder-only、以T5和BART为代表的encoder-decoder、以GPT为代表...
蓝色分支,Decoder-only框架(也叫Auto-Regressive),典型代表如GPT系列/LLaMa/PaLM等 Harnessing the Power of LLMs in Practice 刚听这三种框架名称可能会有点懵逼,不用担心,先感性认识一下。如下所示 横轴代表了输入token,纵轴代表相对应每个位置的输出token 左图为encoder-only,输出token都能看到所有输入token。例如...
LLMs:《A Decoder-Only Foundation Model For Time-Series Forecasting》的翻译与解读 导读:本文提出了一种名为TimesFM的时序基础模型,用于零样本学习模式下的时序预测任务。 背景痛点:近年来,深度学习模型在有充足训练数据的情况下已成为时序预测的主流方法,但这些方法通常需要独立在每个数据集上训练。同时,自然语言处...
In this article we prove that the general transformer neural model undergirding modern large language models (LLMs) is Turing complete under reasonable assumptions. This is the first work to directly address the Turing completeness of the underlying technology employed in GPT-x as past work has ...
Apart from the various interesting features of this model, one feature that catches the attention is its decoder-only architecture. In fact, not just PaLM, some of the most popular and widely used language models are decoder-only.
(2001). Second, the inherent recurrent architecture of RNNs prevents efficient parallelization when encoding, cf. Vaswani et al. (2017).\({}^1\) The original quote from the paper is "Despite their flexibility and power, DNNs can only be applied to problems whose inputs and targets can be...
But now, in the decoder part, we want the algorithm to create one token each time only considering the previous ones already generated. To make this work properly, we need to forbid the tokens from getting information from the right of the sentence. This is done by masking the matrix of ...