Embedding and fine-tuning are two essential concepts related to training neural language models (NLMs).
Embedding is a technique used to represent discrete variables, such as words or tokens, as continuous vectors in a high-dimensional space.
Embeddings capture the semantic and syntactic relationships between words, enabling neural language model to understand the meaning and context of text.
Embedding Layer
In neural language models, the embedding layer is responsible for mapping each input token to its corresponding embedding vector.
The embedding layer is typically the first layer in the model architecture and is initialised with pre-trained weights obtained from large-scale unsupervised learning tasks, such as language modeling.
The embedding layer works as follows
Tokenization: The input text is tokenized into a sequence of tokens that the pre-trained model can understand.
Embedding Lookup: Each token in the input sequence is passed through the embedding layer, which performs a lookup operation to retrieve the corresponding embedding vector. The embedding vectors are learned during the pre-training phase and capture general language knowledge.
Output: The output of the embedding layer is a sequence of embedding vectors, where each vector represents a token in the input sequence. These embeddings are then passed to the subsequent layers of the model for further processing.
Transformer Architecture
The Transformer relies heavily on the concept of self-attention, which allows the model to weigh the importance of different tokens in the input sequence when processing each token.
In the Transformer architecture, the embedding layer plays a crucial role:
Input Embedding: The input tokens are passed through the embedding layer to obtain their corresponding embedding vectors. These embeddings capture the semantic and syntactic information of the tokens.
Positional Encoding: Since the Transformer does not have any inherent notion of token order, positional encodings are added to the input embeddings. Positional encodings are fixed or learned vectors that encode the position of each token in the sequence, allowing the model to understand the relative position of tokens.
Self-Attention: The embeddings (with positional encodings) are then passed through the self-attention mechanism, which computes the attention scores between all pairs of tokens in the sequence. This allows the model to capture dependencies and relationships between tokens, regardless of their distance in the sequence.
Fine-Tuning: Fine-tuning is the process of adapting a pre-trained language model to a specific downstream task, such as sentiment analysis, named entity recognition, or text classification. During fine-tuning, the pre-trained model's parameters, including the embedding layer weights, are updated using a smaller task-specific dataset.
The fine-tuning process involves the following steps:
Tokenization: The input text for the downstream task is tokenized in the same way as during pre-training.
Embedding Lookup: The tokenized input is passed through the pre-trained embedding layer to obtain the corresponding embedding vectors. The embedding layer weights are initialised with the pre-trained values and are fine-tuned along with the rest of the model.
Task-Specific Layers: Additional layers, such as a classification head or a sequence-to-sequence layer, are added on top of the pre-trained model to adapt it to the specific downstream task.
Fine-Tuning: The entire model, including the embedding layer and task-specific layers, is fine-tuned using the task-specific dataset. The model's weights are updated to capture the nuances and patterns specific to the downstream task.
During fine-tuning, the embedding layer adapts the pre-trained embeddings to the target domain or task. The fine-tuned embeddings capture task-specific semantic and syntactic information, which helps the model perform better on the downstream task.
Conclusion
Embedding and fine-tuning are fundamental concepts in training neural language models.
The embedding layer is responsible for mapping input tokens to continuous vector representations, capturing semantic and syntactic relationships.
In the Transformer architecture, embeddings play a crucial role in the self-attention mechanism, enabling the model to understand dependencies between tokens.
Fine-tuning adapts a pre-trained model, including its embedding layer, to a specific downstream task. During fine-tuning, the embedding layer weights are updated along with the rest of the model to capture task-specific information.
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