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What are the key advantages of using LSTMs or GRUs over basic RNNs when handling long sequences?



The key advantages of using LSTMs (Long Short-Term Memory) or GRUs (Gated Recurrent Units) over basic RNNs (Recurrent Neural Networks) when handling long sequences are their ability to mitigate the vanishing gradient problem and capture long-range dependencies. Basic RNNs struggle with long sequences because the gradients used to update the network's weights can diminish exponentially as they are backpropagated through time. This makes it difficult for the network to learn relationships between words or events that are far apart in the sequence. LSTMs and GRUs address this issue by introducing gating mechanisms and memory cells. LSTMs use input, forget, and output gates to control the flow of information into and out of the memory cell, allowing the network to selectively remember or forget information over long periods. GRUs simplify this structure with only update and reset gates, but they still achieve a similar effect. These gating mechanisms allow LSTMs and GRUs to maintain a more stable gradient flow, preventing the gradients from vanishing and enabling the network to learn long-range dependencies more effectively. For example, if a sentence contains a subject and a verb that are separated by several intervening clauses, a basic RNN might struggle to connect the subject and verb due to the vanishing gradient problem. LSTMs and GRUs, however, can use their gating mechanisms to preserve information about the subject in the memory cell, allowing the network to correctly identify the verb that corresponds to the subject, even over long distances. Therefore, LSTMs and GRUs are better suited for handling long sequences because they can mitigate the vanishing gradient problem and capture long-range dependencies more effectively than basic RNNs.