In a sequence-to-sequence model, information is encoded through a two-step process: encoding and decoding.

  1. Encoding: The input sequence is passed through an encoder, typically a recurrent neural network (RNN) such as LSTM or GRU. The encoder processes the input sequence token by token, updating its hidden state at each step. The final hidden state of the encoder contains the encoded information of the input sequence. It captures the contextual information and summarizes the input sequence into a fixed-length vector, often referred to as the "context vector" or "thought vector".

  2. Decoding: The context vector is then passed to the decoder, which is another RNN. The decoder starts generating the output sequence token by token based on the context vector and its own hidden state. At each step, the decoder takes into account the previous generated token and the hidden state from the previous step to predict the next token. This process continues until an end-of-sequence token is generated or a maximum length is reached.

  The encoder-decoder architecture allows the model to learn to encode the input sequence into a fixed-length representation and then generate the output sequence based on that representation. By iteratively updating the hidden states and generating tokens, the model can capture the dependencies and relationships between the input and output sequences.

  It's important to note that the encoding of information is implicitly learned by the model during training. The model doesn't explicitly assign meaning to each token or maintain an explicit mapping between input and output tokens. Instead, it learns to capture the patterns and semantics of the training data through the optimization process.