ashtonohms@gmail.com
What are the disadvantages of transformers when compared to  RNNs?

Transformers and RNNs are both powerful deep learning models that can be used for a variety of natural language processing tasks. However, each model has its own strengths and weaknesses. Here are some disadvantages of transformers when compared to RNNs:

    Sequential processing: RNNs are designed to process sequential data, such as text or speech, one element at a time, which makes them well-suited for tasks that require modeling the temporal dependencies between inputs. Transformers, on the other hand, process all inputs in parallel, which can make them less effective for tasks that require modeling long-term dependencies.

    Computational complexity: Transformers are typically more computationally expensive than RNNs, due to their parallel processing nature and the large number of parameters required to train them. This can make training and inference slower and more resource-intensive.

    Input length limitations: Transformers have a fixed maximum input length, determined by the number of positional embeddings used to encode the input sequence. This can make them less suitable for tasks that require processing very long input sequences, such as document classification or translation.

    Limited interpretability: Transformers can be more difficult to interpret than RNNs, due to their complex attention mechanisms and the large number of layers and parameters involved. This can make it harder to diagnose and understand errors or biases in the model's predictions.

    Data requirements: Transformers often require large amounts of training data to perform well, due to their high-dimensional parameter space and the complexity of the models. This can make them less suitable for tasks with limited training data or in domains where annotated data is scarce or expensive to obtain.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence