Prompt Guide 101

NLP

  1. Named Entity Recognition (NER) Prompt: Identify and label all named entities in the following sentence: 'Barack Obama visited Paris last year.'

Example:

Prompt: Perform NER on the sentence: 'Barack Obama visited Paris last year.'
Expected Output: 
        - Entity: Barack Obama, Label: PERSON
        - Entity: Paris, Label: LOCATION
        - Entity: last year, Label: DATE
  1. Part-of-Speech Tagging Prompt: Provide the part-of-speech tags for each word in the sentence: 'The quick brown fox jumps over the lazy dog.'

Example:

Prompt: Perform POS tagging on the sentence: 'The quick brown fox jumps over the lazy dog.'
Expected Output: 
        - The: DT (Determiner)
        - quick: JJ (Adjective)
        - brown: JJ (Adjective)
        - fox: NN (Noun)
        - jumps: VBZ (Verb)
        - over: IN (Preposition)
        - the: DT (Determiner)
        - lazy: JJ (Adjective)
        - dog: NN (Noun)
  1. Text Classification Prompt: Classify the sentiment of the following review as positive, negative, or neutral: 'The movie was fantastic! I loved every minute of it.'

Example:

Prompt: Classify the sentiment of the review: 'The movie was fantastic! I loved every minute of it.'
Expected Output: Positive Sentiment
  1. Text Summarization Prompt: Generate a concise summary of the following article: [Provide the article text]

Example:

Prompt: Summarize the following article: 'COVID-19 Vaccination: Progress and Challenges in Global Immunization Efforts'
Expected Output: This article discusses the progress and challenges of COVID-19 vaccination worldwide.
  1. Question Answering Prompt: Answer the following question based on the provided passage: [Provide the passage and question]

Example:

Prompt: Answer the question: 'What is the capital of Australia?' based on the provided passage.
Passage: Australia is a country in Oceania. It is known for its unique wildlife and natural landmarks.
Question: What is the capital of Australia?
Expected Output: The capital of Australia is Canberra.
  1. Text Generation Prompt: Compose a short poem about the beauty of nature.

Example:

Prompt: Write a short poem about the beauty of nature.
Expected Output: In the woods so green and vast,
                 Nature's wonders unsurpassed.
                 Rivers flowing, birds in flight,
                 Underneath the moon's soft light.
  1. Machine Translation Prompt: Translate the following sentence from English to French: 'Hello, how are you?'

Example:

Prompt: Translate the following sentence from English to French: 'Hello, how are you?'
Expected Output: Bonjour, comment ça va?
  1. Text Completion Prompt: Complete the sentence: 'The best way to learn a new language is...'

Example:

Prompt: Complete the sentence: 'The best way to learn a new language is...'
Expected Output: The best way to learn a new language is through immersion and practice.
  1. Sentiment Analysis Prompt: Analyze the sentiment of the tweet: 'Just had the most amazing dinner at my favorite restaurant!'

Example:

Prompt: Analyze the sentiment of the tweet: 'Just had the most amazing dinner at my favorite restaurant!'
Expected Output: Positive Sentiment
  1. Speech Recognition Prompt: Transcribe the following audio clip into text: [Provide the audio clip]

Example:

Prompt: Transcribe the following audio clip into text.
Audio Clip: [Audio clip of a person saying, 'Hello, how are you?']
Expected Output: Hello, how are you?
  1. Text Paraphrasing Prompt: Rewrite the following sentence in a more formal style: 'I gotta go to the store.'

Example:

Prompt: Paraphrase the following sentence in a more formal style: 'I gotta go to the store.'
Expected Output: I need to go to the store.
  1. Document Classification Prompt: Classify the type of document based on its content: [Provide the document text]

Example:

Prompt: Classify the type of document based on its content.
Document: This is a legal contract between Party A and Party B for the sale of property.
Expected Output: Legal Contract
  1. Information Extraction Prompt: Extract all the email addresses mentioned in the following text: 'Please send your inquiries to info@example.com or support@example.com.'

Example:

Prompt: Extract all the email addresses mentioned in the following text: 'Please send your inquiries to info@example.com or support@example.com.'
Expected Output: info@example.com, support@example.com
  1. Document Summarization Prompt: Generate a brief summary of the research paper: 'Recent Advances in Artificial Intelligence.'

Example:

Prompt: Summarize the research paper: 'Recent Advances in Artificial Intelligence.'
Expected Output: This paper discusses recent advances in the field of artificial intelligence.
  1. Text Entailment Prompt: Determine whether the following statement logically follows from the given context: [Provide the context and statement]

Example:

Prompt: Determine if the following statement logically follows from the context: 'The sky is blue.'
Context: The sky is clear, and the sun is shining.
Expected Output: Entailment (True)
  1. Text Alignment Prompt: Align the following parallel sentences in English and Spanish: [Provide the sentences]

Example:

Prompt: Align the following parallel sentences in English and Spanish.
English Sentence: I love to travel.
Spanish Sentence: Me encanta viajar.
Expected Output: (English) I love to travel. - (Spanish) Me encanta viajar.
  1. Text Clustering Prompt: Cluster the provided set of documents into different topics or categories.

Example:

Prompt: Cluster the provided set of documents into different topics or categories.
Documents: [List of documents]
Expected Output: Cluster 1: Technology, Cluster 2: Health, Cluster 3: Sports
  1. Text Similarity Prompt: Calculate the similarity between the following two sentences: 'The sun is shining' and 'It is a sunny day.'

Example:

Prompt: Calculate the similarity between the following two sentences: 'The sun is shining' and 'It is a sunny day.'
Expected Output: High similarity
  1. Text Normalization Prompt: Normalize the following text by converting all characters to lowercase and removing punctuation: 'Hello, World!'

Example:

Prompt: Normalize the following text by converting all characters to lowercase and removing punctuation: 'Hello, World!'
Expected Output: hello world
  1. Text Anomaly Detection Prompt: Identify any anomalous sentences or phrases in the provided text: [Provide the text]

Example:

Prompt: Identify any anomalous sentences or phrases in the provided text.
Text: The quick brown fox jumps over the lazy dog. The weather is nice today. ze@th j#mped 0ver th3 l@zy d0g!
Expected Output: ze@th j#mped 0ver th3 l@zy d0g! (Anomalous)
  1. Language Identification Prompt: Detect the language of the following text: 'Bonjour, comment ça va?'

Example:

Prompt: Detect the language of the following text: 'Bonjour, comment ça va?'
Expected Output: French
  1. Entity Linking Prompt: Link all named entities in the text to their corresponding Wikipedia pages.

Example:

Prompt: Link all named entities in the text to their corresponding Wikipedia pages.
Text: Albert Einstein was a famous physicist.
Expected Output: 
                - Entity: Albert Einstein, Link: https://en.wikipedia.org/wiki/Albert_Einstein
  1. Text Dependency Parsing Prompt: Parse the sentence and generate the dependency tree for the following text: 'John eats an apple.'

Example:

Prompt: Parse the sentence and generate the dependency tree for the following text: 'John eats an apple.'
Expected Output: 
        (ROOT
        (S
            (NP (NNP John))
            (VP (VBZ eats)
            (NP (DT an) (NN apple)))
            (. .))
  1. Text Generation with Constraints Prompt: Generate a product description of 50 words with a focus on sustainability.

Example:

Prompt: Generate a product description of 50 words with a focus on sustainability.
Expected Output: Introducing our eco-friendly reusable water bottle made from recycled materials. Embrace sustainability while staying hydrated on the go.
  1. Text Segmentation Prompt: Segment the following text into separate sentences: 'The sun is shining. It's a beautiful day.'

Example:

Prompt: Segment the following text into separate sentences: 'The sun is shining. It's a beautiful day.'
Expected Output: 
        - Sentence 1: The sun is shining.
        - Sentence 2: It's a beautiful day.
  1. Text Expansion Prompt: Expand the following abbreviations in the text: 'I'll be there at 2 p.m.'

Example:

Prompt: Expand the following abbreviations in the text: 'I'll be there at 2 p.m.'
Expected Output: I will be there at 2 in the afternoon.
  1. Intent Detection Prompt: Identify the intent of the user's query: 'What time does the movie start?'

Example:

Prompt: Identify the intent of the user's query: 'What time does the movie start?'
Expected Output: Movie Showtime Inquiry
  1. Text Filtering Prompt: Remove all profanity and offensive language from the text: [Provide the text]

Example:

Prompt: Remove all profanity and offensive language from the text.
Text: This is a *** good movie with great actors!
Expected Output: This is a good movie with great actors!
  1. Text Error Correction Prompt: Correct the spelling and grammar errors in the following sentence: 'I am goin to the store.'

Example:

Prompt: Correct the spelling and grammar errors in the following sentence: 'I am goin to the store.'
Expected Output: I am going to the store.
  1. Text Sentiment Transfer Prompt: Convert the sentiment of the following sentence from positive to negative: 'I love this product.'

Example:

Prompt: Convert the sentiment of the following sentence from positive to negative: 'I love this product.'
Expected Output: I dislike this product.
  1. Text Generation with Style Transfer Prompt: Generate a news headline in a humorous style.

Example:

Prompt: Generate a news headline in a humorous style.
Expected Output: Breaking News: Penguins Take Over Ice Cream Shop, Demand Fish-Flavored Cones!
  1. Text Stylization Prompt: Stylize the following text in a cursive font: 'Hello, World!'

Example:

Prompt: Stylize the following text in a cursive font: 'Hello, World!'
Expected Output: 𝓗𝓮𝓵𝓵𝓸, 𝓦𝓸𝓻𝓵𝓭!
  1. Text Language Adaptation Prompt: Translate the given English text to Spanish while preserving the original meaning.

Example:

Prompt: Translate the given English text to Spanish while preserving the original meaning.
Text: The weather is beautiful today.
Expected Output: El clima está hermoso hoy.
  1. Text Revision Prompt: Revise and improve the following paragraph for clarity and coherence.

Example:

Prompt: Revise and improve the following paragraph for clarity and coherence.
Text: The new product is good. I like it a lot.
Expected Output: The newly launched product is excellent, and I highly recommend it.
  1. Text Coherence Evaluation Prompt: Assess the coherence of the provided text and rate it on a scale of 1 to 5, where 5 indicates high coherence.

Example:

Prompt: Assess the coherence of the provided text and rate it on a scale of 1 to 5, where 5 indicates high coherence.
Text: The moon is shining bright tonight. I need to buy groceries tomorrow.
Expected Output: Coherence Rating: 3 (Moderate coherence)
  1. Text Alignment for Parallel Corpora Prompt: Align the English and Chinese sentences in the parallel corpus for machine translation.

Example:

Prompt: Align the English and Chinese sentences in the parallel corpus for machine translation.
English Sentence: I love reading books.
Chinese Sentence: 我喜欢读书。
Expected Output: (English) I love reading books. - (Chinese) 我喜欢读书。
  1. Text Emotion Recognition Prompt: Detect the emotion expressed in the following sentence: 'I am feeling anxious about the upcoming exam.'

Example:

Prompt: Detect the emotion expressed in the following sentence: 'I am feeling anxious about the upcoming exam.'
Expected Output: Emotion: Anxiety
  1. Text Sarcasm Detection Prompt: Determine if the following statement is sarcastic: 'Oh, great! Another rainy day!'

Example:

Prompt: Determine if the following statement is sarcastic: 'Oh, great! Another rainy day!'
Expected Output: Sarcastic (True)
  1. Text Gender Bias Detection Prompt: Identify any gender bias in the provided text and suggest gender-neutral alternatives.

Example:

Prompt: Identify any gender bias in the provided text and suggest gender-neutral alternatives.
Text: The programmer worked hard on his project.
Expected Output: Suggested Alternative: The programmer worked hard on their project.
  1. Text Inference Prompt: Infer the missing word in the following sentence: 'She was wearing a beautiful ___ dress.'

Example:

Prompt: Infer the missing word in the following sentence: 'She was wearing a beautiful ___ dress.'
Expected Output: She was wearing a beautiful red dress.
  1. Text Image Captioning Prompt: Generate a descriptive caption for the provided image: [Provide the image]

Example:

Prompt: Generate a descriptive caption for the provided image.
Image: [Image of a beach with palm trees and a clear blue sky]
Expected Output: A serene beach with palm trees under a clear blue sky.
  1. **

Text Plagiarism Detection Prompt**: Detect any plagiarized content in the given document.

Example:

Prompt: Detect any plagiarized content in the given document.
Document: [Provide the document text]
Expected Output: No plagiarized content detected.
  1. Text Style Transfer Prompt: Change the writing style of the following text to formal: 'Hey, how's it going?'

Example:

Prompt: Change the writing style of the following text to formal: 'Hey, how's it going?'
Expected Output: Hello, how are you?
  1. Text Explanation Prompt: Explain the reasoning behind the model's answer to the question: [Provide the question and context]

Example:

Prompt: Explain the reasoning behind the model's answer to the question: 'What is the capital of France?'
Context: France is a country located in Western Europe.
Expected Output: The model identified that Paris is the capital of France based on the provided context.
  1. Text Attribute Modification Prompt: Modify the attribute of the following text to reflect a different sentiment: 'This cake is delicious.'

Example:

Prompt: Modify the attribute of the following text to reflect a different sentiment: 'This cake is delicious.'
Expected Output: This cake is tasteless.
  1. Text Intent Expansion Prompt: Expand the user's query by predicting additional information required to answer the question.

Example:

Prompt: Expand the user's query by predicting additional information required to answer the question: 'What are the best restaurants in town?'
Expected Output: What are the best restaurants in town for Italian cuisine?
  1. Text Coherence Restoration Prompt: Reorder the sentences in the following text to improve coherence: [Provide the text]

Example:

Prompt: Reorder the sentences in the following text to improve coherence.
Text: I went to the store. The sun was shining. It was a beautiful day.
Expected Output: The sun was shining. It was a beautiful day. I went to the store.
  1. Text Semantic Role Labeling Prompt: Label the semantic roles of the words in the following sentence: 'The cat chased the mouse.'

Example:

Prompt: Label the semantic roles of the words in the following sentence: 'The cat chased the mouse.'
Expected Output: 
        - cat: Agent
        - chased: Predicate
        - mouse: Patient
  1. Text Conditional Generation Prompt: Generate a story with a happy ending, based on the provided plot: [Provide the initial plot]

Example:

Prompt: Generate a story with a happy ending, based on the provided plot: 'A young girl finds a magical book that takes her on exciting adventures.'
Expected Output: As the young girl continued her magical adventures, she discovered her inner strength and courage, leading to a joyous and happy ending.
  1. Text Style Adaptation Prompt: Adapt the writing style of the following text to match that of a children's storybook.

Example:

Prompt: Adapt the writing style of the following text to match that of a children's storybook.
Text: Once upon a time, in a faraway kingdom, there lived a brave knight.
Expected Output: Once upon a time, in a magical land, there was a brave knight who embarked on exciting quests.
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Contributors: rparth07