While both language models and sequence-to-sequence models operate within the realm of natural language processing, they serve distinct purposes and exhibit different architectural characteristics. A language model focuses on predicting the next token or word in a given sequence, essentially modeling the probability distribution of sequential data. In contrast, a sequence-to-sequence model, also known as an encoder-decoder architecture, maps an input sequence to an output sequence, making it well-suited for tasks such as machine translation, text summarization, and dialogue generation.
