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Prompt templates are a flexible tool that can be adapted to a wide range of tasks. The same templating approach used for one natural language processing task can be extended by defining new templates for other distinct tasks, such as machine translation.

Versatility of Prompt Templates

Prompt templates can be designed for various NLP tasks, including machine translation. To frame translation as a text generation problem, a template can be used to instruct the model. For example, to translate an English sentence into Chinese, the following template could be used, where `{*sentence*}` is a placeholder for the input text:

`{*sentence*} Question: What is the Chinese translation of this English sentence? Answer:`

Example of a Prompt Template for Machine Translation

A software development team currently uses a large language model for code generation with the template: `/* Write a Python function that {*task_description*} */`. They now need to adapt the model to perform a new, distinct task: identifying and explaining potential bugs in a given code snippet. Design a new, effective prompt template for this bug identification and explanation task. Your template should clearly define the input and instruct the model on the desired output format.

Adapting a Prompt Template for a New Task

A development team is using a language model for a specific task: extracting the 'Sender' and 'Subject' from emails. They use the following instructional format: `From the email below, extract the Sender and Subject. 

Email: {*email_body*} 

Sender:`. This has proven very effective. Now, the team wants to use the same model for a new task: writing a one-sentence summary of a news article. An engineer suggests they can simply reuse their existing instructional format by placing the news article text where the email body goes. Evaluate this engineer's proposal. Is this approach likely to be effective? Justify your reasoning.

Evaluating a Prompt Template Adaptation Strategy

A team has successfully implemented a system where a language model classifies customer reviews as 'Positive', 'Negative', or 'Neutral' using a specific instructional format: `Review: {*review_text*} Sentiment:`. The team now wants to use the same language model for a completely different task: summarizing long articles into a single paragraph. Which of the following actions best demonstrates an understanding of how to adapt instructional formats for new tasks?

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