Transformers: An AI Architecture with Self-Attention - A Beginner Freindly Guide
Nithin M A
| Senior Data Analyst | Data Scientist | NLP | Engineer | Artificial Intelligence Practitioner | Generative Artificial Intelligence | LLMs | RAG | Agents |
Introduction: The Rise of Transformers in AI
Transformers have dramatically changed how machines understand language, translate text, and process data. This writing focuses on explaining the key concepts behind transformers, such as their architecture, the problems they solve, and their innovative use of self-attention.
The Shortcomings of RNNs
Before diving into transformers, it’s important to understand their predecessor: Recurrent Neural Networks (RNNs). A recurrent neural network looks very much like a feedforward neural network, except it also has connections pointing backward.Despite RNNs' success in sequence prediction tasks, they struggle with long-term dependencies, slow computation, and vanishing/exploding gradients. These issues make it hard for RNNs to learn from sequences of significant length.
The Introduction of Transformers
The transformer model, introduced by Vaswani et al. in 2017, solves many of the issues inherent in RNNs by completely doing away with recurrence. Instead, it relies on the mechanism of self-attention, which allows it to process the entire sequence of data simultaneously.
Input Embeddings and Positional Encoding
The transformer begins by converting input words into vectors called embeddings. But unlike RNNs, which have an inherent notion of sequence due to their recurrent nature, transformers require positional encoding to understand word order. Using sinusoidal functions, the model encodes the position of each word in a way that is easy for the model to learn.
The Power of Self-Attention
At the heart of the transformer is the self-attention mechanism. This allows each word in the input to interact with every other word, helping the model focus on relevant parts of the input sequence. Self-attention provides flexibility, allowing the model to "attend" to different parts of the sentence when interpreting meaning.
For example, in the sentence "The cat sat on the mat," the word "cat" might attend to "sat" and "mat," while "the" would attend less strongly to the rest of the sentence. This leads to better contextual understanding.
Query, Key, and Value Vectors Self-attention operates on three vectors derived from each input word:
The Transformer employs multiple "heads" of attention, each learning different aspects of the relationships between words. This multi-head attention allows the model to capture various types of dependencies in the data.
领英推荐
Layer Normalization
Decoder and Masked Multi-Head Attention
While the encoder processes the input, the decoder works on the output. It uses masked multi-head attention to ensure that predictions for a certain position in the sequence depend only on the words that have already been generated, ensuring causality.
Training and Inference
During training, the Transformer processes entire sequences in parallel, making it highly efficient. The model learns to predict the next word in a sequence, gradually improving its understanding of language patterns.
Training the transformer involves minimizing cross-entropy loss, which ensures that the predicted words match the target words as closely as possible. During inference, beam search is often used to find the most probable output sequence.
At inference time, the Transformer generates output sequences one word at a time. It uses the encoder's output and previously generated words to predict the next word, repeating this process until it produces an end-of-sequence token.
Advantages of the Transformer
Applications and Impact
The Transformer architecture has become the foundation for numerous state-of-the-art models in NLP, including:
These models have achieved remarkable results in various NLP tasks, from machine translation to question answering and text generation.
Conclusion
The Transformer model represents a paradigm shift in NLP, demonstrating that attention mechanisms alone can outperform traditional recurrent architectures. Its ability to process sequences in parallel, capture long-range dependencies, and provide interpretable results has made it a cornerstone of modern NLP research and applications.
As the field continues to evolve, the Transformer's influence remains strong, inspiring new architectures and pushing the boundaries of what's possible in natural language understanding and generation. The journey that began with "Attention is All You Need" continues to transform the landscape of artificial intelligence and language processing.
Link to Original Source: