When it comes to NLP tasks, the debate between LSTMs and Transformers is ongoing. But what happens when the advantage of parallelism is no longer a factor? In this scenario, which model would you prefer? Assuming both models have the same number of parameters, the question becomes more nuanced.
I’ve consulted with various AI models, including 40, Claude Sonnet, Gemini Flash 2.5, and Grok 3, and I’ll be sharing their responses in the comments. The goal is to explore how to think about different models and their advantages. It’s easy to default to using Transformers, but is that always the best approach?
LSTMs have their own strengths, particularly when it comes to handling sequential data. But Transformers have revolutionized the field of NLP with their ability to handle parallel processing. So, how do we choose between these two powerful models?
In this post, we’ll dive into the pros and cons of each model and explore when to use each one. Whether you’re a seasoned NLP practitioner or just starting out, this discussion is meant to spark a deeper understanding of the models we use and how to choose the right one for the task at hand.