What are the differences between Transformers and LSTMs?

What are the differences between Transformers and LSTMs?

Transformers and LSTMs are both neural network architectures used for sequence data, but they have key differences. Transformers use self-attention mechanisms, allowing them to process data in parallel, while LSTMs are recurrent networks that process data sequentially.

Which one is better for natural language processing tasks?

Transformers generally outperform LSTMs in NLP tasks due to their ability to capture long-range dependencies and parallelize training. Models like BERT and GPT are based on Transformers and have set new benchmarks.

Are there any scenarios where LSTMs might be preferred?

Yes, LSTMs can be preferred in scenarios with limited data or when the sequence length is short, as they are simpler and require less computational power.

Can you give examples of applications for both?

Sure! Transformers are widely used in translation, text generation, and summarization, while LSTMs are often used in time series prediction and speech recognition.