GPT-3 is an autoregressive transformer model with 175 billion parameters. It uses the same architecture/model as GPT-2, including the modified initialization, pre-normalization, and reversible tokenization, with the exception that GPT-3 uses alternating dense and locally banded sparse attention patterns in the layers of the transformer, similar to the Sparse Transformer.
Source: Language Models are Few-Shot LearnersPaper | Code | Results | Date | Stars |
---|
Task | Papers | Share |
---|---|---|
Language Modelling | 76 | 10.03% |
Question Answering | 50 | 6.60% |
Large Language Model | 47 | 6.20% |
In-Context Learning | 33 | 4.35% |
Retrieval | 32 | 4.22% |
Code Generation | 28 | 3.69% |
Prompt Engineering | 26 | 3.43% |
Sentence | 23 | 3.03% |
Text Generation | 19 | 2.51% |