(4)casual language model task like gpt (5)Mask language model task like bert (6)seq2seq language model task (7)next sentence prediction 串行多任务 微调下游任务 参考自原论文和 UniLM: Unified Language Model Pre-training for Natural Language Understanding and Generation Hanscal:UniLM:自然语言理解与...
用Conditional training代表在PHF(橙色实线),SFT+HF(橙色虚线)和MLE的Toxicity的分数,可以看到随tokens个数增加,PHF有明显优势 最后还有一个结论,在红队训练过程中发现的LM对池中adversarial prompt的平均misalignment score(越低越好),对于经过conditional training预处理(实线),只用conditional training finetune的模型(-...
Pre-training Language Model as a Multi-perspective Course Learner Beiduo Chen, Shaohan Huang, Zihan Zhang, Wu Guo, Zhenhua Ling, Haizhen Huang, Furu Wei, Weiwei Deng, Qi Zhang ACL 2023|July 2023 Download BibTex ELECTRA, the generator-discriminator pre-training framework, has achiev...
面向任务:Natural Language Understanding and Generation 论文地址:https://arxiv.org/abs/1905.03197 论文代码:暂未 0-1. 摘要 本文提出一个能够同时处理自然语言理解和生成任务UNIfied pre-trained Language Model (UNILM) 模型。UNILM模型的预训练是基于3个目标:单向LM(包括从左到右和从右到左)、双向LM和sequ...
2.1 Language model pre-training 语言模型一般会从一个无标签的语料库中做预训练,先学习知识;然后针对下游特定任务做精调。这样做的适用性通常比端到端的从头训练要好。 3. Approach 3.1. REALM’s generative process REALM以一些句子为输入,输出是一个分布,即各种可能的预测及其概率。
(1)masked language model(MLM)(类似完形填空一样对一个句子挖掉一个token,然后去预测该token): The masked language model randomly masks some of the tokens from the input, and the objective is to predict the original vocabulary id of the masked word based only on its context. ...
Guu K., Lee K., Tung Z., Pasupat P. and Chang M. REALM: Retrieval-augmented language model pre-training. ICML, 2020. 概 赋予生成模型检索的能力. REALM 如上图所示, 作者希望实现这样一个事情: 给定一个'预测'任务, 如 "The [MASK] at the top of the pyramid", 作者不希望像一般的模型一...
First, hierarchical attentional network with pre-training language model on training data, such as global vectors for word representation and bidirectional ... C Zhao,S Wang,D Li,... - 《Information Sciences》 被引量: 0发表: 2021年 Adaptive Spatio-Temporal Graph Enhanced Vision-Language Represent...
paper阅读:UniLM(Unified Language Model Pre-training for Natural Language Understanding and Generation) 概述: UniLM是微软研究院在Bert的基础上,最新产出的预训练语言模型,被称为统一预训练语言模型。它可以完成单向、序列到序列和双向预测任务,可以说是结合了AR和AE两种语言模型的优点,Unilm在抽象摘要、生成式问题...
This paper presents a new Unified pre-trained Language Model (UniLM) that can be fine-tuned for both natural language understanding and generation tasks. The model is pre-trained using three types of language modeling tasks: unidirectional, bidirectional, and sequence-to-sequence prediction. The uni...