How do transformers handle long-range dependencies compared to traditional neural networks?
Asked on Nov 09, 2025
Answer
Transformers handle long-range dependencies more effectively than traditional neural networks by using self-attention mechanisms, which allow them to weigh the importance of different input tokens regardless of their position in the sequence.
Example Concept: Transformers utilize a self-attention mechanism that computes attention scores for each token in a sequence relative to all other tokens. This allows the model to focus on relevant parts of the input sequence, regardless of their distance from each other. In contrast, traditional neural networks, like RNNs, process sequences sequentially, which can lead to difficulties in capturing dependencies over long distances due to vanishing gradient problems.
Additional Comment:
- Transformers use positional encodings to retain the order of sequences, which is crucial for understanding context.
- The self-attention mechanism in transformers enables parallel processing of tokens, improving efficiency and scalability.
- Long-range dependencies are better captured because each token can directly attend to every other token in the sequence.
- Traditional RNNs and LSTMs may struggle with long sequences due to their sequential nature and gradient issues.
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