Transformers are a type of neural network architecture that has been widely used for natural language processing tasks such as language translation, text summarization, and question-answering. They were introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017 and have since gained widespread adoption in machine learning.

One key advantage of transformers is their ability to effectively handle long-range dependencies in sequential data, such as natural language text. They do this using self-attention mechanisms, which allow the model to weigh different input elements differently when computing the output. This allows transformers to model relationships between input elements far apart in the input sequence, particularly useful for tasks such as language translation where word order is important.

Another advantage of transformers is their high parallelizability, which makes them more efficient to train and run on hardware than many other types of neural networks. They also tend to perform well on a wide range of tasks, making them versatile tools for natural language processing.

One limitation of transformers is that they can be more difficult to interpret than some other types of neural network architectures. This can make it challenging to understand how the model makes decisions and identify potential issues or biases in the model.

Despite this limitation, transformers have become popular for natural language processing tasks due to their strong performance and efficiency. They are likely to continue to be an important tool in the field of machine learning in the coming years.