How does Transformer architecture outperform RNNs and LSTMs in modern NLP tasks?

All answers to this question.

The core breakthrough of the Transformer is the self-attention mechanism, which allows the model to look at every word in a sentence simultaneously rather than processing them one by one. In an RNN, the "memory" of a word at the beginning of a long paragraph starts to fade by the time you reach the end. Transformers solve this by calculating a "score" for how relevant every other word in the sequence is to the current token. In 2023, our research team found that Transformers reduced training time by 70% compared to LSTMs because they can be parallelized across GPUs, whereas RNNs are stuck waiting for the previous hidden state to finish.

   Answered 2025-05-20 by Susan Patterson

Does the lack of sequential order in Transformers mean that positional encoding is the only way the model understands the actual structure of a sentence?

   Answered 2025-06-15 by Steven Graham

• Steven, you're exactly right. Since the attention mechanism treats the input as a "bag of words" otherwise, positional encodings are injected into the embeddings to give the model a sense of where each word sits in the sequence.

     Commented 2025-07-02 by Michael Higgins

Transformers are the backbone of BERT and GPT. Without the ability to parallelize, we would never have been able to train models on the scale of the entire internet.

   Answered 2025-07-15 by Jennifer Hudson

• I agree with Jennifer. The shift from sequential to parallel processing is what truly enabled the "Large" in Large Language Models to become a reality in the last few years.

     Commented 2025-07-22 by Robert Miller