What Is a Transformer?
A Transformer is a neural network architecture introduced by Google researchers in 2017 in the paper Attention Is All You Need.
It uses attention mechanisms to help the model learn relationships between tokens. Unlike recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), which process sequences through recurrent steps, Transformers can process input token representations in parallel during training.
The Transformer architecture combines attention with other components, including feed-forward neural networks, positional information, residual connections, and normalization, to build contextual representations of input tokens.
Why Were Transformers Introduced?
Transformers addressed important limitations of recurrent neural networks (RNNs) in sequence processing.
Two key motivations were:
Learning relationships between tokens: RNNs and LSTMs can struggle to capture dependencies between tokens that are far apart. Transformers use attention mechanisms to let tokens incorporate information from other relevant tokens directly.
Parallel processing: RNNs process tokens sequentially because each hidden state depends on the previous one. Transformers enable parallel computation across input tokens during training, improving computational efficiency.
Although LSTMs improved the ability of recurrent networks to retain information over long sequences, they did not remove the sequential-processing limitation.
What Problem Were Transformers Designed to Solve?
Before Transformers, RNNs and LSTMs were widely used to process sequential data, including natural language. They processed tokens sequentially while maintaining a hidden state that carried information from earlier tokens.
However, these architectures had two important limitations.
1. Difficulty learning long-range dependencies
In recurrent networks, information from earlier tokens must pass through multiple recurrent steps to influence later tokens. During training, gradients can vanish as they propagate through long sequences, making it difficult to learn some long-range relationships. LSTMs mitigate this issue through their cell state and gating mechanisms, but they do not eliminate every difficulty.
2. Limited parallelism
Because each recurrent hidden state depends on the previous state, RNNs and LSTMs cannot compute all hidden states of an input sequence simultaneously. This limits parallel computation during training.
Transformers introduced an attention-based architecture without recurrence. Self-attention enables tokens to incorporate information from other relevant tokens directly, while the absence of recurrent dependencies allows input token representations to be processed in parallel during training.
How RNNs and LSTMs Process Sequences
RNN: A recurrent neural network processes a sequence one token at a time. At each step, it combines the current token representation with the previous hidden state to produce a new hidden state. This state carries a learned representation of information from the sequence processed so far.
Limitation: As sequences become longer, an RNN can struggle to preserve useful information from earlier tokens. During training, vanishing gradients can also make long-range dependencies difficult to learn.
LSTM: Long Short-Term Memory networks extend RNNs with a cell state and three gates: forget, input, and output. These mechanisms regulate which information is retained, added, or exposed. They help preserve relevant information over longer sequences and improve gradient flow.
Remaining limitation: LSTMs still process tokens sequentially because each recurrent step depends on the previous step.
Why Was Sequential Processing a Limitation?
RNNs and LSTMs process sequences through recurrent steps, where each hidden state depends on the previous state. This dependency prevents the model from computing all recurrent hidden states simultaneously.
As a result, these architectures limit parallel computation across sequence positions during training, particularly for long sequences.
Transformers remove recurrence from the core architecture and use self-attention to process input token representations in parallel during training. This enables more efficient use of modern parallel hardware, although Transformer attention also introduces computational and memory costs as sequence length increases.
What Did the 2017 Paper Attention Is All You Need Introduce?
In 2017, Vaswani and colleagues introduced the Transformer architecture in their paper Attention Is All You Need.
The paper proposed an encoder-decoder architecture that relied on attention mechanisms instead of recurrent or convolutional layers for sequence processing.
Its key contributions included:
- Self-attention: Allowed tokens to incorporate information from other positions in a sequence.
- Multi-head attention: Enabled the model to learn different relationships through multiple attention heads.
- Parallelizable computation: Removing recurrence allowed input sequence representations to be processed in parallel during training.
The original Transformer was designed for sequence transduction tasks, particularly machine translation. Its architecture later became the foundation for many language models, including encoder-only, decoder-only, and encoder-decoder models.
How Do Transformers Process Relationships Between Tokens?
Transformers use a mechanism called self-attention to model relationships between tokens in a sequence.
Unlike recurrent networks, which pass information through successive hidden states, self-attention allows each token to incorporate information from other relevant tokens directly.
For each token, the model computes attention scores that determine how much information to gather from other token positions. These scores are converted into weights, which are used to combine information from those tokens and produce updated representations.
As a result, each token can develop a contextual representation that incorporates information from across the sequence. These representations are then processed by other Transformer components, including feed-forward neural networks.
This mechanism helps Transformers learn relationships between distant tokens and provides a flexible way to represent context.
Where Are Transformers Used Today?
Transformers have become a foundational architecture in modern AI, with applications across several domains.
- Natural language processing: Large language models use Transformers for text generation, question answering, coding, summarization, and conversational AI. Transformer-based encoder models are also used for classification and information extraction.
- Machine translation: Encoder-decoder Transformers learn relationships between source and target sequences to translate between languages.
- Computer vision: Vision Transformers process image patches to perform tasks such as image classification and image understanding.
- Speech and audio: Transformer-based architectures support speech recognition, transcription, translation, and audio generation.
- Multimodal AI: These models combine information from modalities such as text, images, audio, and video to support tasks that require understanding multiple forms of input.
Across these applications, the central idea is to learn useful representations and relationships among elements of the input. The specific architecture and training objective depend on the task.
Conclusion
Transformers marked a significant shift in how neural networks process sequential data. By replacing recurrent processing with attention-based mechanisms, they enabled models to learn relationships between tokens more directly while supporting greater parallelism during training.
The idea introduced in Attention Is All You Need in 2017 has since become foundational to modern AI, powering advances in language processing, computer vision, speech, and multimodal systems.
Understanding Transformers is more than learning a particular neural network architecture. It is understanding how a different approach to representing context and relationships can unlock new capabilities in AI.
And this is only the beginning. To understand how modern language models work under the hood, the next step is to explore self-attention in detail: how Query, Key, and Value representations are created, how attention scores are calculated, and how those scores determine the information each token receives.
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