Transformers
GPT
Introduced by Radford et al. in Improving Language Understanding by Generative Pre-TrainingGPT is a Transformer-based architecture and training procedure for natural language processing tasks. Training follows a two-stage procedure. First, a language modeling objective is used on the unlabeled data to learn the initial parameters of a neural network model. Subsequently, these parameters are adapted to a target task using the corresponding supervised objective.
Source: Improving Language Understanding by Generative Pre-Training
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Language Modelling | 88 | 11.17% |
| Large Language Model | 50 | 6.35% |
| Question Answering | 35 | 4.44% |
| Prompt Engineering | 25 | 3.17% |
| Text Generation | 24 | 3.05% |
| Retrieval | 21 | 2.66% |
| Decision Making | 20 | 2.54% |
| In-Context Learning | 20 | 2.54% |
| Sentence | 19 | 2.41% |
Adam
Attention Dropout
BPE
Dense Connections
Discriminative Fine-Tuning
Dropout
GELU
Layer Normalization
Linear Warmup With Cosine Annealing
Multi-Head Attention
Residual Connection
Scaled Dot-Product Attention
Softmax
Weight Decay
