Answer: Predicts the next word using encoded context

## Explanation In Transformer-based translation models like T5 or MarianMT: - **The encoder** processes the input sentence and creates contextual embeddings - **The decoder** generates the output translation one token at a time, using: - The encoded context from the encoder (via cross-attention) - Previously generated tokens (via self-attention with masking) - The encoded representations to predict the next word in the target language Option B is correct because the decoder's primary function is to autoregressively generate the output sequence by predicting the next word based on the encoded input context and previously generated tokens. **Why other options are incorrect:** - **A**: Encoding the input is the encoder's role, not the decoder's - **C**: Removing noise from text is not the primary function of the decoder in translation models - **D**: Tokenization is typically a preprocessing step, not the decoder's main role

Author: Ritesh Yadav