GPT1

GPT-1, short for Generative Pre-trained Transformer 1, is a language model developed by OpenAI and released in 2018. It introduced the idea of pre-training a single large neural network on unlabeled text and then fine-tuning it on downstream tasks, a form of transfer learning for natural language processing.

Architecturally, GPT-1 uses a transformer decoder with 12 layers, a hidden size of 768, and 12 attention heads, totaling about 117 million parameters. It was pre-trained with a causal language modeling objective on a large unlabeled corpus (the BooksCorpus). After pre-training, the model was fine-tuned on each task by adding a small task-specific output layer and training with labeled data, allowing the same pre-trained model to adapt to multiple tasks.

Evaluation showed that a single pre-trained model could be adapted to a variety of NLP tasks, including sentiment analysis, semantic similarity, and question answering, through fine-tuning rather than task-specific architectural changes. This approach demonstrated that unsupervised pre-training could improve performance across diverse benchmarks with relatively limited labeled data.

GPT-1 laid the groundwork for the GPT series and contributed to the broader adoption of large pre-trained language models in NLP. It highlighted the potential of generative pre-training as a general-purpose foundation for downstream tasks, while also illustrating limitations related to the scale of data and resources required for training such models, which motivated subsequent iterations like GPT-2 and GPT-3.