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通过自回归空白填充目标进行训练,因此可以直接解决这个任务。我们在Yahoo Answers数据集(Yang等人,2017)上评估GLM,并与专门设计用于文本填充的Blank Language Model(BLM)(Shen等人,2020)进行比较。从表5的结果可以看出,GLM大幅优于先前的方法(1.3到3.9 BLEU),在该数据集上达到了最先进的结果。我们注意到GLMDoc略逊...
GLM RoBERTa可以实现匹配Seq2Seq BART模型的性能,并且优于T5和UniLMv2。 表3 和表 4:GLMLarge 可以在二次生成任务上实现与其他预训练模型相匹配的性能。GLMSent 的性能优于 GLMLarge,而 GLMDoc 的性能略低于 GLMLarge。 3.3. 文字填充 表5:GLM 大大优于以前的方法(1.3 到 3.9 BLEU),并在此数据集上取得了...
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...
【LLM系列之GLM】GLM: General Language Model Pretraining with Autoregressive Blank Infilling,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
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。
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 ...
GLM is a General Language Model pretrained with an autoregressive blank-filling objective and can be finetuned on various natural language understanding and generation tasks. Please refer to our paper for a detailed description of GLM: GLM: General Language Model Pretraining with Autoregressive Blank...
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 ...
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 predict spans, which results in performance gains over BERT and T5 on NLU tasks....