How do transformers handle long sequences effectively without losing context?
Asked on Oct 05, 2025
Answer
Transformers handle long sequences effectively by using mechanisms like self-attention and positional encoding to capture dependencies between tokens, regardless of their distance within the sequence.
Example Concept: Transformers use self-attention to compute a weighted representation of the entire input sequence, allowing each token to attend to every other token. This mechanism helps maintain context over long distances by assigning higher weights to relevant tokens, thus preserving important information. Additionally, positional encoding is added to input embeddings to provide the model with information about the order of tokens, which is crucial for understanding sequences.
Additional Comment:
- Self-attention allows transformers to process all tokens simultaneously, which is more efficient than sequential models like RNNs.
- Positional encoding ensures that the model understands the sequence order, which is not inherently captured by the self-attention mechanism.
- Transformers can be scaled to handle very long sequences by using techniques like sparse attention or memory-augmented networks.
- These mechanisms enable transformers to excel in tasks like language translation and text summarization, where long-range dependencies are common.
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