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Grammaticality judgment can be reframed as a text generation task by using a prompt template, which often uses a placeholder like `{*sentence*}` for the text to be evaluated. The prompt's structure can vary significantly. For instance, given a sentence, several formats are possible:

1.  **Question First**: The query precedes the sentence.
    `Question: Is this sentence grammatically correct? John seems happy today.`

2.  **Sentence First with Answer Cue**: The sentence is followed by the question and a cue for the model's response.
    `John seems happy today. Question: Is this sentence grammatically correct? Answer:`

3.  **Command-based Instruction**: The prompt is phrased as a command followed by the sentence.
    `Read the following sentence, and answer whether it is grammatically correct. LLMs are powerful models but are expensive to build.`

4.  **Sentence First with Natural Question**: The sentence is presented, followed by a more conversational question, often on a new line.
    `LLMs are powerful models but are expensive to build.
Does this sentence make sense grammatically?`

Examples of Prompt Templates for Grammaticality Judgment

The newline character, denoted by `\n`, is a special character that functions as a line break in text. When processed within an input string, it signals a move to the start of the next line.

Newline Character (\n)

To control the output of a language model, a prompt can include explicit instructions that specify the desired answer format and define the meaning of each possible response. For a grammaticality judgment task, this could involve instructing the model to answer only with 'Yes' or 'No', and clarifying that 'Yes' confirms the sentence is grammatically correct, while 'No' indicates it contains grammatical errors.

Defining Answer Format and Semantics in Prompts

Analyze the structure of each prompt template below and match it with the correct description of its format.

Analyze the following scenario and the provided prompt. Explain why this prompt structure is likely leading to ambiguous responses from the language model. Then, propose a revised, more effective prompt that is designed to elicit a clear, binary judgment (e.g., 'Correct' or 'Incorrect').

Improving Prompt Design for Grammaticality Judgment

A researcher wants to test a language model's ability to judge the grammar of various sentences. They decide that for their experiment, the prompt must present the sentence to be evaluated *before* asking the model to make a judgment. Which of the following prompt designs meets this specific structural requirement?

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