What is Decoder?
Decoder in the Transformer model utilizes the hidden states from the encoder to generate sequential output tokens. The decoder layers receive input from their own previous outputs, through positional encoding and embedding matrices. Each decoder layer comprises attention blocks, feed-forward networks, and normalization layers. Six decoder layers are stacked together to form a decoder stack. Data from the encoder is also passed to each decoder layer. This process produces the final hidden states of the decoder, representing the output results. This process is also applicable to applications like language translation, through softmax layers which generate output probabilities.
Theories:
Multi-head attention:
Value: Utilizes multiple attention inputs simultaneously to enhance learning efficiency. Pros: Enables the model to focus on different parts of input data simultaneously, improving representation capability. Cons: Requires significant computational resources, potentially increasing model complexity. Title suggestion: “Enhancing learning efficiency through multi-head attention in Transformer model”.
Feed-forward network:
Value: Classic neural network used to transform hidden states. Pros: Flexible in learning nonlinear representations of data. Cons: Prone to overfitting if not carefully tuned. Title suggestion: “Diverse representation through feed-forward neural network in Transformer model”.
Softmax layer:
Value: Converts hidden states into probabilities for feasible output classes. Pros: Produces clear probability distributions for prediction. Cons: Susceptible to vanishing gradient problem during training. Title suggestion: “Accurate prediction through Softmax layer in Transformer model”.
- Understanding Attention in Transformers
- Encoder in the Transformer Model
- Decoder in Transformer Model
- Training and Inference with Transformers
- The Power of Attention Mechanism in Transformers
- Unveiling the Potential of Transformers in NLP
- The Role and Pros and Cons of Positional Encoding in Transformer Architecture
- Advancements of Transformer Model and Attention Mechanism in NLP
- Enhancing Transformer Model Performance: In-depth Analysis and Practical Applications
- Comparison Analysis Between Google’s PALM and PALM-2 Language Models
