而chatglm就是GLM(General Language Model Pretraining with Autoregressive Blank Infilling)架构实现的,因此作为了解chatglm的一部分,先学习下GLM。 一. 概述 NLP预训练模型的架构大致可以分为三类:自编码模型(Bert),自回归模型(GPT),encoder-decoder架构(T5)。然而,没有任何一个架构能在三个主流NLP任务上都达到最...
GLM: General Language Model Pretraining with Autoregressive Blank Infilling(完整版) 摘要 已经有各种类型的预训练架构,包括自编码模型(例如BERT),自回归模型(例如GPT)和编码器-解码器模型(例如T5)。然而,没有一个预训练框架对三个主要类别的所有任务(自然语言理解(NLU),无条件生成和有条件生成)都表现最佳。我们...
GLM(General Language Model Pretraining with Autoregressive Blank Infilling)是一种自回归语言模型,通过空白填充的方式进行自回归建模。这种模型以随机顺序预测span,并辅以二维的位置编码来捕捉文本中的结构信息。与传统的自回归语言模型相比,GLM的独特之处在于它采用了空白填充的方式,即随机在输入文本中选择一段span置为...
GLM:General Language Model PretrainingwithAutoregressive Blank Infilling》论文链接:httpsarxiv1:httpsgithubcomhttps://githubcom/THUDM/ChatGLM-6Bhuggingface链接:https://huggingface.co/THUDM/chatglm-6b 摘要 目前,已经有各种类型的预训练架构,包括自编码模型(例如BERT),自回归模型(例如GPT)和编码器-解码器模型...
ChatGPT已经火了一段时间了,国内也出现了一些平替,其中比较容易使用的是ChatGLM-6B:https://github.com/THUDM/ChatGLM-6B,主要是能够让我们基于单卡自己部署。ChatGLM的基座是GLM: General Language Model Pretraining with Autoregressive Blank Infilling论文中提出的模型,接下来我们来看看。
【LLM系列之GLM】GLM: General Language Model Pretraining with Autoregressive Blank Infilling 摘要 目前,已经有各种类型的预训练架构,包括自编码模型(例如BERT),自回归模型(例如GPT)和编码器-解码器模型(例如T5)。然而,没有一个预训练框架对三个主要类别的所有任务(自然语言理解(NLU),无条件生成和有条件生成)都...
language understanding (NLU), unconditional generation, and conditional generation. We propose a General Language Model (GLM) based on autoregressive blank infilling to address this challenge. GLM improves blank filling pretraining by adding 2D positional encodings and allowing an arbitrary order to ...
However, none of the pretraining frameworks performs the best for all tasks of three main categories including natural language understanding (NLU), unconditional generation, and conditional generation. We propose a General Language Model (GLM) based on autoregressive blank infilling to address this ...
However, none of the pretraining frameworks performs the best for all tasks of three main categories including natural language understanding (NLU), unconditional generation, and conditional generation. We propose a General Language Model (GLM) based on autoregressive blank infilling to address this ...
GLM: General Language Model Pretraining with Autoregressive Blank Infilling (ACL 2022) Zhengxiao Du*, Yujie Qian*, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, Jie Tang (*: equal contribution) News: We release ChatGLM-6B, an open pre-trained language model with 6 billion parameters opt...