why the maximum effective context length is limited by the number of layers ?

The maximum effective context length is limited by the number of layers in a neural network because as the number of layers increases, the network becomes more complex and the amount of information that can be propagated through the network is limited. This means that the effective context length, which is the length of the input sequence that can be used to make predictions or classifications, becomes smaller as the number of layers increases.

Each layer in a neural network processes a portion of the input sequence and extracts features from that portion, which are then used as input to the next layer. As the input sequence is processed through multiple layers, the network gradually builds up a representation of the entire input sequence. However, if the network is too deep, the gradients that are used to update the weights of the network during training can become very small, which can cause the information to decay as it is propagated through the layers. This means that the information from the early layers may be lost by the time it reaches the later layers, which limits the effective context length that can be used.

Additionally, as the number of layers in a network increases, the computational cost of the network also increases, making it more difficult to train and deploy. Therefore, there is a trade-off between the number of layers in a network and the effective context length that can be used. In practice, it is often necessary to balance these factors to achieve the best performance on a given task. Techniques such as residual connections and skip connections can be used to help mitigate the impact of the vanishing gradients problem and enable deeper networks to be used, which can in turn increase the effective context length that can be used.