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...
Pre-trained language models (PLMs) are first trained on a large dataset and then directly transferred to downstream tasks, or further fine-tuned on another small dataset for specific NLP tasks. Early PLMs, such as Skip-Gram [1] and GloVe [2], are shallow neural networks, and their word e...
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...
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...
In this paper, we aim to study the behavior of pre鈥恡rained language models (PLMs) in some inference tasks they were not initially trained for. Therefore, we focus our attention on very recent research works related to the inference capabilities of PLMs in some selected tasks such as ...
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. ...
【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; ...
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
在自然语言处理领域中,预训练语言模型(Pre-trained Language Models)已成为非常重要的基础技术。 目前互联网上存在大量的现代汉语BERT模型可供下载,但是缺少古文的语言模型。 为了进一步促进古文研究和自然语言处理的结合,我们发布了古文预训练模型GuwenBERT。
Recent advances in image tokenizers, such as VQ-VAE, have enabled text-to-image generation using auto-regressive methods, similar to language modeling. However, these methods have yet to leverage pre-trained language models, despite their adaptability to various downstream tasks. In this work, we...