GPT-1 (Generative Pre-trained Transformer 1) was the first in the series of Generative Pre-trained Transformer models developed by OpenAI. Released in 2018, GPT-1 utilized the transformer architecture and was trained on a large corpus of text data. The key innovation of GPT-1 was the concept of pre-training a language model on a large dataset and then fine-tuning it for specific tasks. GPT-1 demonstrated significant improvements in various NLP tasks, laying the foundation for subsequent models.
- Architecture: Transformer
- Training Data: BooksCorpus (over 7,000 unpublished books)
- Parameters: 110 million
GPT-2 (Generative Pre-trained Transformer 2) is the successor to GPT-1 and was released in 2019. GPT-2 is much larger than GPT-1, with 1.5 billion parameters, and was trained on a more diverse and larger dataset. The model demonstrated the ability to generate coherent and contextually relevant text, even with minimal input. GPT-2's release sparked discussions around the ethical implications of advanced AI models due to its ability to generate realistic and human-like text.
- Architecture: Transformer
- Training Data: WebText dataset (8 million web pages)
- Parameters: 1.5 billion
- Notable Features: Zero-shot learning, text generation
GPT-2 remains a significant milestone in the development of NLP models and has inspired further advancements in the field, leading to even more powerful models like GPT-3 and GPT-4.
For more detailed information, you can explore the OpenAI GPT-2 paper.