I keep reading that LSTMs and GRUs are "dead" in the age of Transformers. Can someone explain in plain English why the Attention mechanism is so much better at handling long-range dependencies in text? I understand that LSTMs have a "memory," so why did the industry shift so quickly toward models like BERT and GPT? I’m trying to justify a hardware upgrade to my boss for GPU-based training.
3 answers
The primary reason is parallelization. LSTMs process text sequentially—one word at a time—which makes them incredibly slow to train on large datasets because you can't easily distribute the work across multiple GPUs. Transformers use the "Attention" mechanism to look at every word in a sentence simultaneously. This allows the model to understand the context of a word based on its relationship with every other word, regardless of how far apart they are. This "global" view prevents the vanishing gradient problem that plagued LSTMs when trying to remember information from the beginning of a long paragraph while processing the end.
Is your primary concern the training speed or the actual accuracy of the model? For very short sequences, LSTMs can still be quite efficient and less resource-heavy than a massive Transformer.
Transformers simply scale better. As you add more data and more parameters, the performance of Transformers continues to climb, whereas LSTMs tend to plateau much earlier.
spot on, Laura. The scalability is exactly why we've seen the jump from millions to billions of parameters in the latest language models we see today.
Michael, we are mostly dealing with long-form medical reports, so capturing context across several pages is our main goal. Based on what Jessica said, it sounds like the sequential nature of LSTMs is exactly what’s holding us back. We need that "global" attention to link symptoms mentioned at the start to a diagnosis at the end. I think the speed of training on a GPU cluster will be the final selling point for my boss to move away from our current CPU-based LSTM setup.