Only if your model islarge enough 这种零样本能力是从哪里来的?【没看懂】 假设:在预训练期间,训练数据集隐含地包含不同任务的混合 假设:多任务训练可实现零样本泛化 Multi-task fine-tuning using a PLM 【没看懂】 对某些类型的任务进行微调,对其他类型的任务执行零样本推断 小结:Use natural language prompts...
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...
Pre-trained language modelTagalogPOS taggingPre-trained language models (PLMs) for Tagalog can be categorized into two kinds: monolingual models and multilingual models. However, existing monolingual models are only trained in small-scale Wikipedia corpus and multilingual models fail to deal with ...
根据GPT-3的思想,我们使用prompt-based微调方法。prompt-based方法将下游的任务视为一种mask language model。因此其需要为这些prompt生成mask标签,GPT-3提供的是人工标注方法,可能会由于一定的baise导致陷入局部最优问题,因此我们选择使用T5模型来自动创建prompt模板 另外,我们也采用in-context的微调方法,即随机从语料中...
三、Revisit of Pre-trained Language Model BERT MLM:从输入中随机mask部分token,并预测该token; NSP:预测两个句子是否存在next关系; Whole Word Masking(WWM):mask整个词,而不是单独的word piece token; 详细可参考博客:BERT ERNIE Entity-level Masking:改进MLM,每次mask一个命名实体,而不是随机...
chinesebertpre-trainedrobertagpt2pre-trained-language-models UpdatedJul 22, 2024 Python zjunlp/KnowLM Star1.2k Code Issues Pull requests An Open-sourced Knowledgable Large Language Model Framework. deep-learningmodelsinstructionsenglishchinesellamaloralanguage-modelreasoningbilingualpre-trainingpre-trained-model...
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...
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; ...
where they called the Permutation Language Model (PLM). Another is to change the autoencoding language model into an autoregressive one, which is similar to the traditional statistical language models.(XLNET主要做了两方面的改变。首先,最大化所有输入排列的对数似然,它成为重排列语言建模PLM。其次,XLNET...
Second, the text tokens in the image-text datasets are too simple compared to normal language model pre-training data, making any small randomly initialized language models achieve the same perplexity with larger pre-trained ones, and causes the catastrophic degradation of language models' capability...