MLM-based pre-trained: BERT 系列 (BERT, RoBERTa, ALBERT) 1.2.1Autoregressive Language Models (ALMs): Complete the sentence given its prefix 自监督学习:从任何其他部分预测输入的任何部分 Transformer-based ALMs:由多层transformer层堆叠组成 1.2.2Masked Language Models (MLMs): Use the unmasked words to...
How can we extend a pre-trained model tomany language understanding tasks, without labeled or additional unlabeled data? Pre-trainedlanguage models (PLMs) have been effective for a wide range of NLP tasks. However, existing approaches either require fine-tuningon downstream labeleddatasetsor manually...
This chapter presents the main architecture types of attention-based language models, which describe the distribution of tokens in texts: Autoencoders similar to BERT receive an input text and produce a contextual embedding for each token. Autoregressive language models similar to GPT receive a subseq...
Making Pre-trained Language Models Better Few-shot Learners 陈丹琦团队提出的一种改进GPT-3的模型,其可以扩展到任意的预训练模型上,并可以在小样本情景下更好的进行微调。 简要信息: 核心要点: Template的构建:基于T5模型生成和排序方法生成离散template; Verbalizer的构建:基于排序搜索方法; 通过交叉验证方...
【预训练语言模型】MacBERT: Revisiting Pre-trained Models for Chinese Natural Language Processing 简要信息: 一、动机 在一些较为复杂的QA任务上,BERT等一系列的预训练语言模型可以达到很高的效果; 训练transformer-based预训练模型比较困难; 大多数的语言模型是基于英语的,很少有工作致力于提升中文语言模型的提升; ...
Must-read papers on prompt-based tuning for pre-trained language models. nlpmachine-learningaipromptprompt-toolkitbertpre-trained-language-modelsprompt-learningprompt-based UpdatedJul 17, 2023 Top2Vec learns jointly embedded topic, document and word vectors. ...
DIFFERENTIABLE PROMPT MAKES PRE-TRAINED LANGUAGE MODELS BETTER FEW-SHOT LEARNERS DifferentiAble pRompT (DART),预训练的语言模型+反向传播对提示模板和目标标签进行差异优化 可微提示(DART)模型的体系结构与MLM预训练和常规微调进行了比较,其中Ti和Yi是词汇表中未使用的或特殊的标记。我们利用语言模型中的一些参数作为...
Pre-trained Language Models Can be Fully Zero-Shot Learners Xuandong Zhao, Siqi Ouyang, Zhiguo Yu, Ming Wu, Lei Li ACL 2023|July 2023 How can we extend a pre-trained model to many language understanding tasks, without labeled or additional unlabeled data? Pre-trained lang...
让预训练语言模型在少样本学习方面表现得更好 一、引言 近年来,深度学习技术取得了巨大成功,特别是在自然语言处理领域。预训练语言模型作为一种有效方法,已经在许多应用场景中展现出出色的
Large language models (LLMs) have substantially pushed artificial intelligence (AI) research and applications in the last few years. They are currently able to achieve high effectiveness in different natural language processing (NLP) tasks, such as machine translation, named entity recognition, text ...