Given the sequence of words in a sentence, which of the following RNN variations would be best suited for understanding long-term dependencies in that sequence, especially when the context of earlier words must be maintained for later words?

LSTM models keep a memory cell that updates slowly, so old information can still influence later steps. Other options are incorrect because GRU is a simpler version of LSTM, but it still shares the same short‑term limitation because its single gate can’t hold as much long‑term memory as LSTM’s separate gates; Vanilla RNNs compute a new hidden state by multiplying the previous state with a weight matrix.