Improving Language Understanding By Generative
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Improving Language Understanding by Generative Pre …
s3-us-west-2.amazonaws.comImproving Language Understanding by Generative Pre-Training Alec Radford OpenAI alec@openai.com Karthik Narasimhan OpenAI karthikn@openai.com Tim Salimans OpenAI tim@openai.com Ilya Sutskever OpenAI ilyasu@openai.com Abstract Natural language understanding comprises a wide range of diverse tasks such
![arXiv:2112.15283v1 [cs.CV] 31 Dec 2021](/cache/preview/c/8/9/2/1/9/c/e/thumb-c89219ce53d02972e27bd3f6ad3863e9.jpg)
arXiv:2112.15283v1 [cs.CV] 31 Dec 2021
arxiv.orgWhile most of VLP methods focus on pre-training for the vision-language understanding tasks, some works have noticed the importance of pre-training for improving the performance of cross-modal generation tasks, mainly image captioning task. Zhou et al. [7] propose the first unified model to improve the performance of both understanding and
BERT: Pre-training of Deep Bidirectional Transformers for ...
aclanthology.orgLanguage Understanding Jacob Devlin Ming-Wei Chang Kenton Lee Kristina Toutanova Google AI Language fjacobdevlin,mingweichang,kentonl,kristoutg@google.com Abstract We introduce a new language representa-tion model called BERT, which stands for Bidirectional Encoder Representations from Transformers. Unlike recent language repre-
Language Models are Unsupervised Multitask Learners
cdn.openai.comnatural language sequences in order to better predict them, regardless of their method of procurement. If a language model is able to do this it will be, in effect, performing unsupervised multitask learning. We test whether this is the case by analyzing the performance of language models in a zero-shot setting on a wide variety of tasks. 2.1.