Pre-trained language modelBERTGPTWith the rapid progress in Natural Language Processing (NLP), Pre-trained Language Models (PLM) such as BERT, BioBERT, and ChatGPT have shown great potential in various medical NLP tasks. This paper surveys the cutting-edge achievements in applying PLMs to ...
论文名:Pre-trained Language Models for Text Generation A Survey 作者:Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jian-Yun Nie, Ji-Rong WenAuthors Info & Claims 发布时间:2024-04-25 引用次数: 338 …
论文:Pre-trained Models for Natural Language Processing: A Survey 首先简要介绍了语言表示学习及相关研究进展; 其次从四个方面对现有 PTM (Pre-trained Model) 进行系统分类(Contextual、Architectures、Task Types、Extensions); 再次描述了如何将 PTM 的知识应用于下游任务; 最后展望了未来 PTM 的一些潜在发展方向。
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 (PLMs) refer to neural networks that are trained on large-scale unlabeled corpora and can be further fine-tuned for various downstream tasks. These models, such as those used in NLP, contain a significant amount of linguistic knowledge in their parameters, making them...
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
跨模态预训练任务包括Masked Language Modeling (MLM)、Masked Region Prediction (MRP)和Image-Text Matching (ITM)。MLM和MRP有助于学习图像和文本之间的细粒度相关性，而ITM在粗粒度级别上使二者进行对齐，即要求模型确定图像和文本是否匹配并输出对齐概率。跨模态对比学习（CMCL）输入图像和文本匹配的正...
Paper tables with annotated results for How Vision-Language Tasks Benefit from Large Pre-trained Models: A Survey
First, we review several main challenges in vision-language tasks and discuss the limitations of previous solutions before the era of pre-training. Next, we summarize the recent advances in incorporating pre-trained models to address the challenges in vision-language tasks. Finally, we analyze the...
Pre-trained language models have been the de facto paradigm for most natural language processing (NLP) tasks. In the biomedical domain, which also benefits from NLP techniques, various pre-trained language models were proposed by leveraging domain datasets including biomedical literature, biomedical soci...