Could someone explain the difference between encoder-only and encoder-decoder models in the context of question answering?

Encoder-only models are well-suited for extractive question answering tasks, where the model identifies the most relevant span of text as the answer.
Encoder-decoder models are better for open-ended questions like “Why is the sky blue?” — they can synthesize information and produce a natural, explanatory answer. The encoder builds a representation of the input, and the decoder generates new content based on that representation.

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