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Question Answering is a significant discipline within Natural Language Processing because many other complex problems, including reasoning tasks, can be effectively reframed and solved using a question-answering format.

Importance of Question Answering in NLP

A prominent application of the question-answering framework is in structuring complex reasoning tasks. By defining these challenges in a QA format, models are prompted to perform inference and logical deduction to arrive at a correct answer.

Reasoning Tasks as Question Answering

Based on the principle that many complex language processing challenges can be structured as a question-answering task, describe how you would reframe the sentiment analysis problem described in the case study into a question-answering format. Provide a specific example of a review and the corresponding question you would ask a model to determine its sentiment.

Reframing an NLP Task

A prevailing view in Natural Language Processing is that a sufficiently advanced question-answering system could solve a wide range of other complex tasks, such as logical reasoning and summarization, by reframing them as questions. Critically evaluate this viewpoint. In your answer, discuss at least one major advantage and one potential limitation or challenge of this approach.

Critique of the Question-Answering Framework

A research team is developing a new large language model. They decide to allocate a significant portion of their resources to improving the model's question-answering capabilities, even though their primary goal is to build a system that can perform complex logical reasoning. Which of the following statements best justifies this strategic decision?

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