Category: 02. Disadvantage

If sequences are padded to a fixed length that is too short, you might lose important contextual information from longer sequences.

Padding sequences can result in higher memory usage, as you’re effectively increasing the memory footprint of your data. This can become an issue when working with large datasets.

In some cases, padding can introduce noise or disrupt the natural sequence characteristics, potentially affecting the model’s ability to capture important patterns in the data.

Choosing the appropriate padding length can be challenging. Padding too much might lead to unnecessary computation, while padding too little might result in loss of information. This introduces a hyperparameter that needs to be tuned carefully.

When padding sequences to a fixed length, the added zeros or tokens contribute to the computation load during training and inference, potentially slowing down the process.

Padding sequences with zeros or special tokens can introduce extra information into the data. While this might not be a concern in some cases, it can impact the performance of models when padding is excessive.