Improving Language Understanding by Generative Pre-Training
时间: 2023-04-01 12:01:10 浏览: 137
你好,关于“Improving Language Understanding by Generative Pre-Training”,我可以回答你的问题。这是一种基于生成式预训练的自然语言处理技术,旨在提高语言理解能力和语言生成能力。该技术已经在许多自然语言处理任务中取得了显著的成果,包括文本分类、命名实体识别、情感分析等。
相关问题
将上面第四章的内容补全,表格的内容从你回答中的内容缺定
| 模型名称 | 发布时间 | 论文/技术报告 | 开发公司/组织 |
| --- | --- | --- | --- |
| GPT | 2018年6月 | [Improving Language Understanding by Generative Pre-Training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) | OpenAI |
| GPT-2 | 2019年2月 | [Language Models are Unsupervised Multitask Learners](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf) | OpenAI |
| GPT-3 | 2020年6月 | [Language Models are Few-Shot Learners](https://arxiv.org/pdf/2005.14165.pdf) | OpenAI |
| ChitGPT | 2021年3月 | N/A | CSDN |
注意,目前 ChitGPT 的论文或技术报告并未公开发布。
有关ChatGPT的参考文献
以下是关于ChatGPT的一些参考文献:
1. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.
2. Zhang, Y., Sun, Y., Galley, M., Chen, Y. C., Brockett, C., Gao, X., ... & Dolan, B. (2019). Dialogpt: Large-scale generative pre-training for conversational response generation. arXiv preprint arXiv:1911.00536.
3. Keskar, N. S., Mangipudi, S. A., & Sohoni, M. M. (2020). Transfer learning in natural language processing. arXiv preprint arXiv:2007.01852.
4. Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., ... & Brew, J. (2020). Transformers: State-of-the-art natural language processing. In Advances in Neural Information Processing Systems (pp. 11742-11754).
5. Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. URL https://s3-us-west-2. amazonaws. com/openai-assets/researchcovers/languageunsupervised/language_understanding_paper. pdf.