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 |
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Task | Papers | Share |
---|---|---|
Language Modelling | 82 | 10.79% |
Large Language Model | 49 | 6.45% |
Question Answering | 48 | 6.32% |
Prompt Engineering | 30 | 3.95% |
Retrieval | 30 | 3.95% |
Code Generation | 28 | 3.68% |
In-Context Learning | 28 | 3.68% |
Sentence | 23 | 3.03% |
Benchmarking | 18 | 2.37% |