Bidirectional RNNs are a type of recurrent neural network architecture that processes data in both forward and backward directions, allowing them to capture context from both past and future states in sequence modeling tasks. This design is particularly useful for tasks like language processing, where understanding both preceding and succeeding words can improve the accuracy of predictions.