Transformer Models

Transformer models represent a significant breakthrough in natural language processing (NLP) and have rapidly become the foundation for state-of-the-art generative AI applications. Introduced by Vaswani et al. in 2017, the Transformer architecture revolutionised how machines process sequential data using self-attention mechanisms, allowing for more efficient and scalable models than traditional recurrent neural networks (RNNs).

Architecture of Transformer Models

Self-Attention Mechanism

• Definition: Self-attention allows the model to weigh the importance of different words in a sentence relative to each other, regardless of their position. This mechanism captures long-range dependencies and context more effectively than RNNs.

• Scaled Dot-Product Attention: The core of self-attention involves calculating a weighted sum of input values, where the similarity between input queries and keys determines the weights. The process includes scaling the dot products and applying a softmax function to obtain attention scores.

Encoder-Decoder Structure

• Encoder:

  • Layers: Comprises multiple identical layers, each consisting of a self-attention mechanism and a feedforward neural network.

  • Function: The encoder processes input sequences and generates context-aware representations.

• Decoder:

  • Layers: Similar to the encoder, but with an additional layer to attend to the encoder's output.

  • Function: The decoder generates output sequences by attending to the previous outputs and the encoder's representations.

Positional Encoding

• Definition: Since Transformers do not have an inherent sense of word order, positional encoding is added to the input embeddings to provide information about the position of each word in the sequence.

• Function: This function enables the model to capture the order of words, which is crucial for understanding a language's syntax and semantics.

Figure – The Pixel Recurrent Neural Network evolution process

Training Transformer Models

Data Preparation

• Tokenisation: Converting text into a sequence of tokens (words or subwords) that the model can process. Standard tokenisation techniques include Byte Pair Encoding (BPE) and WordPiece.

• Padding and Masking: Creating padding sequences of the same length and using masking to prevent the model from attending to padded positions.

Optimisation

• Loss Function: A cross-entropy loss function is typically used to compare the predicted output sequence to the actual sequence.

• Gradient Descent: optimisation algorithms like Adam minimise the loss function and update the model's parameters.

Training Process

• Batch Training: Training on batches of data to improve computational efficiency and stabilise learning.

• Teacher Forcing: A technique where the actual target sequence is used as the following input during training, improving convergence and accuracy.

Transformer models have transformed the landscape of generative AI and natural language processing, enabling various applications from text generation to image processing. Understanding their architecture, training processes, and challenges provides a comprehensive view of their capabilities and potential. In the next section, we will explore Diffusion Models, another type of generative AI model, examining their unique approaches and applications.

Learn more about Generative AI Models, mainly Challenges and Future Directions for each main model, in our article:

https://buildingcreativemachines.substack.com/p/generative-ai-models-challenges-and