In the context of Sequence Transduction Models, how can the integration of Long Short-Term Memory (LSTM) networks and attention mechanisms help mitigate the issue of overfitting during training on complex datasets?

LSTM layers keep track of long‑term patterns in a sequence, while attention lets the model look only at the most useful parts of the input. Other options are incorrect because Cutting the model’s size does not solve overfitting; it just makes the model too weak to learn the true patterns; Adding more parameters gives the model more freedom to fit the training data exactly, which usually makes overfitting worse.