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Sequence-to-sequence (seq2seq) models, which utilize both an encoder and a decoder, are a standard framework for text generation tasks. This approach is suitable for applications like machine translation, summarization, question answering, and dialogue generation, where a source text is mapped to a target text. Models like T5 and mBART are prominent examples of pre-trained seq2seq models. This framework is versatile, allowing both Natural Language Understanding (NLU) and Natural Language Generation (NLG) tasks to be addressed and fine-tuned within the same architecture.

Seq2seq Models for Text Generation

A development team is evaluating different model architectures for a variety of natural language tasks. For which of the following tasks would a standard architecture composed of an encoder (to process an input text) and a decoder (to generate an output text) be the LEAST direct and efficient choice?

Based on the scenario below, evaluate the suitability of the proposed two-stage architectural approach for the task of abstractive text summarization. Justify your evaluation by explaining the key strengths of this design for this specific application.

Architectural Suitability for News Summarization

A team is building a system that takes a user's question and a short paragraph of text as input, and then generates a concise answer based *only* on the information in that paragraph. Explain why a model architecture that first processes the input text and question together and then generates the answer sequence is well-suited for this task. In your explanation, describe the distinct roles of the two main components of such an architecture.

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