Comprehensive Guide to Prompting Large Language Models (LLMs) for NLP Tasks

This section explores various examples of prompting Large Language Models (LLMs) for Natural Language Processing (NLP) tasks. The intent is to provide clear, simple illustrations demonstrating how to guide LLMs to solve a variety of NLP problems, rather than prescribing rigid prompt structures.

3.1.4 Prompting for NLP Tasks

3.1.4.1 Text Classification

Text classification is a fundamental NLP task involving the assignment of predefined labels to a given text. A common example is polarity classification in sentiment analysis, where the goal is to classify text as positive, negative, or neutral.

Basic Prompt Example

Analyze the polarity of the following text and classify it as positive, negative, or neutral.

Text: The service at the restaurant was slower than expected, which was a bit frustrating.

LLM Response: The polarity of the text can be classified as positive.

Improvement: While the response is accurate, it's a descriptive sentence rather than a clean label. LLMs are generative, so their output might not directly map to discrete labels. A label mapping system is often needed to extract structured outputs like "positive."

Cloze Task Prompt for Consistency

To improve consistency, reframe the classification as a cloze task:

The polarity of the text is ____.

LLMs can fill in the blank with one of the desired labels. A more deterministic method involves computing:

$$ \text{label} = \text{argmax}_{y \in Y} \text{Pr}(y|x) $$

Where $Y = {\text{positive, negative, neutral}}$ and $\text{Pr}(y|x)$ is the model's probability for each label given the input $x$.

Constrained Output Prompt

This prompt directly requests a specific format for the output:

Analyze the polarity of the following text. Text: The service at the restaurant was slower than expected, which was a bit frustrating. What is the polarity of the text? Just answer: positive, negative, or neutral.

LLM Output:

Positive

Detailed Prompt with Definitions

Providing category definitions enhances accuracy, especially for novel classification tasks:

Analyze the polarity of the following text and classify it as positive, negative, or neutral. Here’s what each category represents:

• Positive: Indicates happiness, satisfaction, or admiration.
• Negative: Reflects sadness, anger, frustration, or criticism.
• Neutral: Conveys informational or indifferent tones.

Text: The service at the restaurant was slower than expected, which was a bit frustrating. What is the polarity of the text?

3.1.4.2 Information Extraction (IE)

Information Extraction (IE) involves identifying structured information such as entities, events, or relationships from unstructured text.

Named Entity Recognition (NER)

NER detects and classifies elements like names of people, places, organizations, dates, etc.

Prompt for Extracting Person Names:

Identify all person names in the provided text.

Text: For Tom Jenkins, CEO of the European Tourism Organisation...

LLM Output:

Tom Jenkins

Prompt for Full NER:

Identify and classify all named entities in the text. List each entity with its type on one line.

Text: For Tom Jenkins, CEO of the European Tourism Organisation...

LLM Output:

Tom Jenkins – Person Name
European Tourism Organisation – Organization
UK – Location
Europe – Location
2024 – Date

Relation Extraction

This task focuses on understanding how identified entities relate to each other.

Prompt Example:

Given a text and a list of named entities, describe how each entity is contextually related.

Text: For Tom Jenkins, CEO of the European Tourism Organisation... Named Entities: Tom Jenkins, European Tourism Organisation, UK, Europe, 2024

LLM Output Example:

Tom Jenkins is the CEO of the European Tourism Organisation, indicating a professional relationship and leadership role.

General Template for IE Tasks

A flexible template for various IE tasks:

You will be provided with a text. Your task is to {task-description}

Text: {text}

Possible tasks include:

• Extract keywords
• Extract events
• Detect coreference links

3.1.4.3 Text Generation

Text generation encompasses producing new content, either by extending an input (completion) or transforming it (paraphrasing, summarizing, etc.).

Text Completion

Prompt for Story Continuation:

You are a writer. Continue the following story.

Start: One sunny afternoon, Lily discovered a tiny, golden key...

Prompt for Conversational Completion:

You are provided with a conversation. Please complete it.

Tourist: Could you take me to the downtown museum, please? Taxi Driver: Of course! First time in the city? Tourist: Yes, any must-see places?

Generating Text Based on Specifications

Prompt for Regulated Chinese Poem:

You are a poet. Please write a traditional Chinese poem.

• Theme: Spring’s rejuvenation
• Structure: Five-character regulated poem
• Emotion: Happiness and renewal

Code Generation Example

Prompt:

Write a Python function to calculate the average of a list of numbers.

LLM Output:

def calculate_average(numbers):
    if numbers:
        return sum(numbers) / len(numbers)
    else:
        return 0

3.1.4.4 Text Transformation

Text transformation tasks convert text from one form to another, such as translation, summarization, or tone conversion.

Translation Prompt

Prompt:

Translate the following text from English to Spanish.

Text: The quick brown fox jumps over the lazy dog.

Output:

El rápido zorro marrón salta sobre el perro perezoso.

Summarization Prompt

Prompt:

Summarize this article in no more than 50 words.

Article: In recent years, urban areas have been facing challenges such as rising pollution, traffic congestion, and increased demand on infrastructure. These issues impact the quality of life for residents and the sustainability of city development. Addressing these requires innovative solutions in urban planning and resource management.

Output:

Urban areas face sustainability issues, including rising pollution, traffic congestion, and increased infrastructure demand. Addressing these requires innovative urban planning and resource management solutions.

Style Transfer Prompt

Prompt:

Rewrite this text in a formal tone.

Text: Hey, what’s up? Long time no see!

Output:

Hello, how have you been? It has been quite some time since we last met!

3.1.4.5 Question Answering (QA)

Question Answering (QA) tasks involve generating answers to queries, which can be open-ended or multiple-choice.

General QA Prompt Template

Prompt:

Question: {question} Answer:

Example from MMLU Benchmark

(Z, *) is a group with a * b = a + b + 1. The inverse of a is: (A) 0 (B) -2 (C) a - 2 (D) (2 + a) * -1

LLM Output:

D

Example from GSM8K Dataset (Math Word Problems)

Prompt with Detailed Reasoning and Final Answer:

Q: Jerry’s two daughters each play 8 games. Each team practices 4 hours per game. Each game lasts 2 hours. What is the total number of hours Jerry’s daughters spend on games and practices?

LLM Output:

Total game hours: 8 games * 2 hours/game = 16 hours per daughter. Total for two daughters = 16 * 2 = 32 hours.
Total practice hours: 8 games * 4 hours/game = 32 hours per daughter. Total for two daughters = 32 * 2 = 64 hours.
Total hours = 32 (games) + 64 (practices) = 96 hours. #### 96

Another Example:

Q: Mary bought six apples. For each apple she ate, she planted two trees. How many apples did she eat?

Output:

The problem states Mary planted 8 trees. Since she plants 2 trees per apple eaten, she ate 8 trees / 2 trees/apple = 4 apples. The initial statement that she bought six apples is context, but the key information for the answer is the number of trees planted. #### 4

Few-shot Prompting

In this approach, the LLM is provided with multiple solved examples. The model learns the format, reasoning style, and structure from these demonstrations. Symbols like #### and ≪...≫ are often used to distinguish between detailed steps and final answers.

3.2 Advanced Prompting Methods (Preview)

Advanced prompting techniques can further improve LLM performance, especially in complex or multi-step reasoning tasks. These include chain-of-thought prompting, self-consistency, and reinforcement learning from human feedback (RLHF).

Conclusion

This guide demonstrates that with carefully crafted prompts, LLMs can perform a wide variety of NLP tasks effectively. Whether through simple classification, complex entity relationships, or generative writing, the key lies in clarity, context, and control within the prompt structure.

SEO Keywords:

• Prompting LLMs for NLP tasks
• Text classification using large language models
• Named entity recognition with GPT prompts
• Information extraction with LLMs
• Text generation prompts for AI models
• Prompt engineering for text transformation
• Chain-of-thought prompting examples
• Few-shot learning in LLMs for QA tasks
• NLP text summarization with LLM prompts
• Advanced prompting techniques for LLM performance

Interview Questions:

1. What are the key components of an effective prompt for text classification using LLMs?
2. Explain how a cloze-style prompt can improve label accuracy in classification tasks.
3. How can you design prompts for named entity recognition and relation extraction? Provide examples.
4. What are some techniques to guide LLMs in producing structured outputs for information extraction?
5. Describe how LLMs can be prompted to perform text transformation tasks such as summarization, translation, and tone conversion.
6. What is the benefit of providing detailed definitions and context in a prompt, especially for nuanced classification?
7. How does few-shot prompting differ from zero-shot prompting, and when should each be used?
8. What are some strategies for prompting LLMs to generate code or perform math-based reasoning tasks?
9. Why is formatting important in LLM prompts, especially for generating clean and structured answers?
10. What are advanced prompting techniques like chain-of-thought prompting, and how do they improve LLM reasoning?