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Language Models are Few-Shot Learners
Ref: https://arxiv.org/abs/2005.14165
This research paper introduces GPT-3, a large language model developed by OpenAI and Johns Hopkins University. The paper details GPT-3's architecture, training data, and performance across numerous natural language processing tasks, focusing on its ability to perform well in zero-shot, one-shot, and few-shot learning settings. Results show GPT-3 achieves state-of-the-art performance on some tasks, though limitations such as biases and potential for misuse are also addressed. The authors analyze data contamination issues and explore GPT-3's capabilities in tasks involving reasoning and novel word usage. Finally, the study concludes with a discussion of the broader societal implications of such powerful language models.
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By KnowledgeDBRef: https://arxiv.org/abs/2005.14165
This research paper introduces GPT-3, a large language model developed by OpenAI and Johns Hopkins University. The paper details GPT-3's architecture, training data, and performance across numerous natural language processing tasks, focusing on its ability to perform well in zero-shot, one-shot, and few-shot learning settings. Results show GPT-3 achieves state-of-the-art performance on some tasks, though limitations such as biases and potential for misuse are also addressed. The authors analyze data contamination issues and explore GPT-3's capabilities in tasks involving reasoning and novel word usage. Finally, the study concludes with a discussion of the broader societal implications of such powerful language models.