In the context of self-attention and Transformer models, encoding and decoding refer to the processes of transforming input sequences into meaningful representations and then generating output sequences from these representations. Here’s a detailed breakdown:
Encoding
Purpose: The encoding process takes the input sequence and converts it into a high-dimensional space, capturing its semantic meaning and relationships between tokens.
Steps in Encoding:
1. Input Embeddings:
- Convert input tokens into continuous vector representations (embeddings). These embeddings are usually supplemented with positional encodings to maintain the order of tokens.
2. Positional Encoding:
- Add positional encodings to the embeddings to provide information about the position of each token in the sequence.
3. Self-Attention Layers:
Apply multiple layers of self-attention. Each layer consists of:
Multi-Head Self-Attention: Each head in the multi-head attention mechanism learns different aspects of relationships between tokens.
Feed-Forward Neural Network: A fully connected neural network applied to each token's representation.
Residual Connections and Layer Normalization: These help in training deep networks effectively by allowing gradients to flow through the network without vanishing or exploding.
4. Output of Encoding:
- The final output of the encoding layer is a sequence of vectors, each representing a token in the input sequence, enriched with contextual information.
Decoding
Purpose: The decoding process generates the output sequence based on the encoded representation of the input sequence. This is typically used in tasks like translation, text generation, or summarization.
Steps in Decoding:
1. Target Embeddings:
Convert target tokens (the output sequence) into embeddings, often with the same dimensionality as the input embeddings.
2. Positional Encoding:
Add positional encodings to the target embeddings to maintain the order of tokens in the output sequence.
3.Self-Attention and Encoder-Decoder Attention:
Apply multiple layers of self-attention and encoder-decoder attention. The encoder-decoder attention mechanism helps the decoder focus on relevant parts of the input sequence.
Masked Multi-Head Self-Attention: Prevents the decoder from attending to future tokens in the sequence (to maintain the autoregressive property).
Encoder-Decoder Attention: Allows the decoder to attend to the encoder’s output, enabling it to generate relevant outputs based on the input context.
4.Feed-Forward Neural Network:
Apply a feed-forward neural network to each token’s representation in the decoder.
5.Output Layer:
Transform the final decoder output into probabilities over the vocabulary. This is typically done using a linear layer followed by a softmax function.
6.Prediction:
Generate the next token in the sequence by sampling from the output probabilities, repeating the process until the sequence is complete.
Example Workflow
Consider translating the sentence "I love machine learning" from English to French:
1. Encoding:
Input: "I love machine learning"
- Convert to embeddings and add positional encodings.
- Pass through multiple encoder layers with self-attention and feed-forward networks.
2. Decoding:
- Initial Token: Start token (e.g., "<SOS>")
- Convert to embeddings and add positional encodings.
- Pass through multiple decoder layers with masked self-attention and encoder-decoder attention.
- Output probabilities for the next token, generate the token (e.g., "J'aime").
3. Repeat: Continue generating tokens until the end-of-sequence token (e.g., "<EOS>") is produced.
Summary of Key Points
Encoder: Processes input sequences to capture their meaning.
Decoder: Generates output sequences based on encoded input and previous tokens in the output sequence.
Self-Attention: Captures relationships within the sequence.
Encoder-Decoder Attention: Links encoder output with decoder input, enabling context-aware generation.
This process is central to many state-of-the-art models in natural language processing, such as GPT, BERT, and the original Transformer architecture.
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