You can think of encoder/decoder models as specifically addressing the translation problem. They are also known as sequence-to-sequence models. Take the task o

You can think of encoder/decoder models as specifically addressing the translation problem. They are also known as sequence-to-sequence models. Take the task of translation. A translator needs to keep in mind the original text and the translation so far in order to predict the next translated token. The original text is encoded, and the translation so far is passed into the decoder to generate the next translated token. The next token is appended to the translation and the process repeats autoregressively. Decoder-only models use just the decoder architecture of encoder/decoders. They are prompted and generate completions autoregressively. Encoder-only models use just the encoder architecture which you can think of similarly to embedding. A task here is, producing vectors where vector distance is related to the semantic similarity of the input documents. This can be useful for retrieval tasks among other things. You can of course translate using just the decoder, by constructing a "please translate this from A to B, <original text>" prompt and generating tokens just using the decoder. I'll leave it to people with more expertise than I do describe the pros and cons of these.