Prompt Templates

The prompting method generates paraphrases by sending input texts to a causal language model together with a prompt template. This page documents every built-in template and explains how to select or customise them.

Zero-shot prompts

Zero-shot templates describe the desired output style directly in the prompt. They use a single placeholder, [DOCUMENT SEGMENT], which is replaced with the input text at generation time.

All zero-shot templates are stored in ZS_PROMPT_BANK.

Key	Description
wikipedia_paraphrase	Paraphrase in Wikipedia-style. From Becker et al. (2024).
finephrase_article	Rewrite as a magazine-style feature article with an engaging lead and narrative storytelling.
finephrase_commentary	Summarise the text and add an expert commentary on its implications and limitations.
finephrase_discussion	Reformulate as a teacher-student dialogue that explains the key points.
finephrase_explanation	Rewrite with explicit scientific or logical explanations of the underlying concepts and mechanisms.
finephrase_faq	Convert into a comprehensive FAQ with self-contained answers.
finephrase_math	Create a mathematical word problem from the text’s numerical data, with a step-by-step solution.
finephrase_narrative	Rewrite as a narrative emphasising temporal sequence and causal relationships.
finephrase_table	Organise the key information into a markdown table, then generate a question-answer pair based on it.
finephrase_tutorial	Rewrite as a step-by-step tutorial or instructional guide.
humanize_llm-as-coauthor_original	Humanise machine-generated text by introducing typos, slang, hashtags, and varied casing (no emojis). From Zhang et al. (2024).
formal_reif	Rewrite the text in a more formal tone. From Reif et al. (2022).
simple_reif	Rewrite the text in simpler language. From Reif et al. (2022).
simple_kew	Simplify a complex sentence for non-native English speakers. From Kew et al. (2023).
complex_kew	Rewrite a simple sentence in a more complex and sophisticated style.
passive_reif	Rewrite the text switching between active and passive voice. Few-shot prompt with examples. From Reif et al. (2022).
caps_reif	Rewrite the text in ALL CAPS.
lowcaps_reif	Rewrite the text in all lower case.
text_emojis_reif	Rewrite the text including text emojis like :-) or ;-D.
less_common_verbs_reif	Rewrite the text using less common verbs.

The finephrase_* templates are taken from the FinePhrasing tool by HuggingFace.

Default selection

When no explicit prompt selection is made, the method cycles through the templates listed in DEFAULT_PROMPTS.

from diversify_text import diversify

# Zero-shot: uses the default templates, no style examples needed.
results = diversify("The cat sat on the mat.", methods=["prompting"])

Selecting specific prompts

Pass a list of keys via method_kwargs to pick specific templates:

results = diversify(
    "The cat sat on the mat.",
    methods=["prompting"],
    method_kwargs={"prompting": {"prompt_keys": ["finephrase_faq", "finephrase_tutorial"]}},
)

Few-shot prompts

Few-shot templates combine style examples from the DEFAULT_STYLE_BANK with a prompt that instructs the model to match the demonstrated writing style. They use three placeholders:

• [DOCUMENT SEGMENT] — the input text

• [STYLE EXAMPLES] — a formatted list of example sentences from the style bank

• [STYLE NAME] — the human-readable name of the style

All example-based templates are stored in EXAMPLE_BASED_PROMPT_BANK.

Key	Description
style_transfer	Paraphrase the text to match the writing style demonstrated in the examples. Preserves information while shifting style.
humanize_transfer	Humanise machine-generated text using the stylistic patterns from the examples. Inspired by Zhang et al. (2024).

Automatic few-shot selection

When styles is provided without explicit prompt_keys, the method automatically uses the style_transfer template:

results = diversify(
    "The experiment was conducted in a controlled lab setting.",
    methods=["prompting"],
    method_kwargs={
        "prompting": {
            "styles": ["informal_tinystyler"],
        }
    },
)

To use a different few-shot template, pass it via prompt_keys:

results = diversify(
    "The experiment was conducted in a controlled lab setting.",
    methods=["prompting"],
    method_kwargs={
        "prompting": {
            "styles": ["informal_tinystyler"],
            "prompt_keys": ["humanize_transfer"],
        }
    },
)

Custom prompt banks

You can replace the entire prompt bank with your own templates. Each template must contain the [DOCUMENT SEGMENT] placeholder:

custom_bank = {
    "simple": "Rewrite the following text in simpler words: [DOCUMENT SEGMENT]",
    "formal": "Rewrite the following text in a formal academic tone: [DOCUMENT SEGMENT]",
}

results = diversify(
    "The cat sat on the mat.",
    methods=["prompting"],
    method_kwargs={"prompting": {"prompt_bank": custom_bank}},
)

Placeholder reference

Placeholder	Used in	Description
[DOCUMENT SEGMENT]	All templates	Replaced with the input text to paraphrase.
[STYLE EXAMPLES]	Few-shot only	Replaced with formatted example sentences from a style set.
[STYLE NAME]	Few-shot only	Replaced with the human-readable style name (e.g. “informal”).