Transformers
Transformers are a deep learning model architecture first introduced by Vaswani et al. in 2017. Unlike recurrent networks (RNNs) or LSTMs, Transformers rely on an attention mechanism that allows models to evaluate the relationships between all words in a sequence simultaneously, rather than step by step.
Core idea
- Self-attention: each token attends to every other token, capturing contextual dependencies.
- Multi-head attention: multiple attention “heads” focus on different aspects of relationships.
- Encoder-decoder structure: encoders build representations, decoders generate predictions.
Strengths
- Scalability → handles very large datasets.
- Superior performance in NLP tasks.
- Foundation for large language models (LLMs) like GPT, BERT, and T5.
Applications
- Language tasks: machine translation, summarization, question answering.
- Computer vision: Vision Transformers (ViTs) competing with CNNs.
- Multimodal AI: models that process both text and images (e.g., CLIP, DALL·E).
Transformers revolutionized modern AI by replacing recurrence with attention. Instead of processing words one at a time, they allow the model to “look” at the entire sequence simultaneously. This shift dramatically improved training efficiency and opened the door to scaling models to billions of parameters.
One of the key strengths is the self-attention mechanism, which weighs the importance of each token relative to others. For instance, in the sentence “The cat sat on the mat because it was warm,” the model can connect “it” to “mat” correctly, something traditional RNNs often struggled with.
Beyond NLP, Transformers are proving to be a general-purpose architecture. Vision Transformers (ViT) have matched or surpassed convolutional networks on image classification benchmarks, and protein sequence models like AlphaFold rely on Transformer variants to capture structural dependencies. Researchers now explore adaptations for audio, time-series forecasting, and even reinforcement learning, confirming that the Transformer is not just a model—it’s an entire paradigm.
Key References
- Vaswani, A. et al. (2017). Attention Is All You Need. NeurIPS.
