Problems with RNN and how attention weights solved this?

Tthe attention mechanism is indeed inspired by the idea of context in Recurrent Neural Networks (RNNs), but it represents a more flexible and powerful way to manage context.

Here’s how it evolved from RNNs:

RNNs and Context:

• RNNs are designed to process sequences (like sentences or time-series data) by maintaining a hidden state that carries information from previous time steps. This hidden state is used to capture the context from the earlier parts of the sequence.
• In an RNN, the context is passed from one step to the next in the form of a hidden state, and this hidden state gets updated as new data comes in. This means that the model relies on the current hidden state to remember previous context.

Problem with RNNs:

• The key issue with RNNs (especially vanilla RNNs and even LSTMs/GRUs) is that as the sequence gets longer, the model struggles to remember distant information due to problems like the vanishing gradient problem.
• Even though LSTMs and GRUs were designed to solve some of these issues, RNNs still tend to focus on the most recent parts of the sequence and can forget important earlier parts, especially in long sequences.

Attention Mechanism:

• The attention mechanism was introduced as a way to explicitly focus on different parts of the input sequence when making predictions, instead of relying only on the hidden state of an RNN.
• It does this by computing weights (based on how relevant each part of the sequence is to the current prediction) for each element in the sequence. This allows the model to “attend” to specific parts of the input, regardless of their position in the sequence.

So, while RNNs pass along context through the hidden state from one time step to the next, attention allows the model to look directly at all the elements in the sequence and decide which ones are most important for making the current prediction.

Why Attention is a Key Improvement:

• Global Context: Attention mechanisms don’t just rely on the most recent information or the hidden state at a single time step. Instead, they can focus on any part of the sequence, meaning they can handle long-range dependencies more effectively.