How do transformers differ from traditional neural networks in handling sequential data?
Asked on Oct 27, 2025
Answer
Transformers differ from traditional neural networks in their ability to handle sequential data by using self-attention mechanisms, which allow them to process entire sequences simultaneously rather than sequentially, enabling more efficient parallelization and capturing long-range dependencies more effectively.
Example Concept: Traditional neural networks, like RNNs, process sequential data step-by-step, which can lead to difficulties in capturing long-range dependencies due to vanishing gradients. Transformers, however, use self-attention to weigh the importance of different elements in the input sequence simultaneously, allowing them to capture dependencies regardless of their distance in the sequence. This approach not only improves performance on tasks involving long sequences but also enables parallel processing, making transformers more computationally efficient.
Additional Comment:
- Transformers use positional encodings to retain the order of the sequence, as self-attention alone does not consider position.
- Self-attention allows transformers to focus on relevant parts of the sequence, improving context understanding.
- The parallel processing capability of transformers leads to faster training times compared to RNNs.
- Transformers have become the backbone of many state-of-the-art models in NLP, such as BERT and GPT.
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