3 Introduction to Large Language Models: Architecture of Large Language Models
Architecture of Large Language Models
3.1 Transformer Architecture
Introduction to Transformers:
Transformers are a type of deep learning architecture introduced in the paper "Attention is All You Need" by Vaswani et al. (2017).
They have become the backbone of many state-of-the-art models in natural language processing, including Large Language Models (LLMs) like GPT and BERT.
Key Components of Transformers:
Self-Attention Mechanism: Allows the model to weigh the importance of different words in a sequence when processing each word, enabling capturing long-range dependencies.
Multi-Head Attention: Utilizes multiple attention mechanisms in parallel to capture different aspects of the input sequence.
Positional Encoding: Provides positional information to the model, enabling it to understand the order of words in a sequence.
Feed-Forward Neural Networks: Process the output of the attention mechanism to generate the final representations of words or tokens.
Encoder-Decoder Architecture:
Transformers typically employ an encoder-decoder architecture for tasks like machine translation and text generation.
The encoder processes the input sequence, while the decoder generates the output sequence based on the encoder's representations.
The encoder and decoder consist of multiple layers of self-attention and feed-forward neural networks.
Benefits of Transformers:
Parallelization: Transformers can process input sequences in parallel, making them highly efficient and scalable.
Capturing Long-Term Dependencies: The self-attention mechanism allows transformers to capture dependencies between distant words in a sequence, addressing one of the limitations of recurrent neural networks (RNNs).
Flexibility: Transformers can handle variable-length input sequences and generate variable-length output sequences, making them suitable for a wide range of tasks in NLP.
3.2 Self-Attention Mechanism
Overview of Self-Attention:
Self-attention is a mechanism that allows a model to weigh the importance of different words in a sequence when processing each word.
It computes a weighted sum of the embeddings of all words in the sequence, where the weights are determined dynamically based on the similarity between words.
Components of Self-Attention:
Query, Key, and Value: In self-attention, each word in the sequence is associated with three vectors: a query vector, a key vector, and a value vector.
Attention Scores: The similarity between a query vector and key vectors determines attention scores, which represent the importance of each word in the sequence.
Weighted Sum: The attention scores are used to compute a weighted sum of the value vectors, resulting in the attended representation for each word.
Multi-Head Attention:
Multi-head attention extends the self-attention mechanism by performing multiple attention operations in parallel.
It allows the model to attend to different parts of the input sequence simultaneously, capturing different aspects of the context.
3.3 Encoder-Decoder Architecture
Role of Encoder and Decoder:
In an encoder-decoder architecture, the encoder processes the input sequence and generates a fixed-length representation (context vector) of the input.
The decoder takes this context vector as input and generates the output sequence step by step.
Sequence-to-Sequence Modeling:
Encoder-decoder architectures are commonly used for sequence-to-sequence tasks such as machine translation and text summarization.
The encoder processes the input sequence (e.g., source language sentence), while the decoder generates the output sequence (e.g., target language translation).
Transformer-based Encoder-Decoder:
Transformers have been successfully applied to sequence-to-sequence tasks by adopting the encoder-decoder architecture.
Both the encoder and decoder consist of multiple layers of self-attention and feed-forward neural networks, enabling the model to capture complex relationships and dependencies between input and output sequences.