Few-shot learning with prompts in NLP

Introduction

Few-shot learning with prompts helps a model learn new tasks quickly using only a few examples. It saves time and data by guiding the model with simple instructions and examples.

You want the model to understand a new task but have very little labeled data.

You want to quickly test how well a language model can perform a task without full training.

You want to customize a model's behavior by showing it a few examples in the prompt.

You want to save resources by avoiding full retraining of a model for every new task.

Syntax

NLP

prompt = "Task description and examples\nInput: example input\nOutput: example output\nInput: new input\nOutput:"

The prompt includes a short task description and a few example input-output pairs.

The model generates the output after the last 'Output:' based on the examples.

Examples

This prompt shows two examples of English to French translation. The model will try to translate the new input after 'Output:'.

NLP

prompt = "Translate English to French:\nInput: Hello\nOutput: Bonjour\nInput: Goodbye\nOutput:"

Here, the prompt teaches the model to classify sentiment with two examples.

NLP

prompt = "Sentiment analysis:\nInput: I love this movie\nOutput: Positive\nInput: This is bad\nOutput:"

Sample Model

This code uses a GPT-2 model to perform few-shot learning for sentiment analysis. It shows two examples in the prompt and asks the model to classify a new sentence.

NLP

from transformers import pipeline

# Load a text generation model pipeline
generator = pipeline('text-generation', model='gpt2')

# Create a few-shot prompt for sentiment analysis
prompt = (
    "Sentiment analysis:\n"
    "Input: I love this movie\nOutput: Positive\n"
    "Input: This is bad\nOutput: Negative\n"
    "Input: The food was great\nOutput:"
)

# Generate output with max length to capture the answer
result = generator(prompt, max_length=len(prompt.split()) + 10, num_return_sequences=1)

# Extract generated text after the prompt
generated_text = result[0]['generated_text']
output = generated_text[len(prompt):].strip().split('\n')[0]

print(f"Prompt:\n{prompt}")
print(f"Model output: {output}")

Important Notes

Few-shot learning depends on clear and relevant examples in the prompt.

Model size and quality affect how well few-shot learning works.

Few-shot learning is useful when you cannot fine-tune a model fully.

Summary

Few-shot learning uses a few examples in a prompt to teach a model a new task.

This method saves time and data compared to full training.

Clear examples and instructions in the prompt help the model perform better.

Practice

1. What is the main idea behind few-shot learning with prompts in NLP?

easy

A. Fine-tuning a model with many epochs on labeled data

B. Training a model with a large dataset from scratch

C. Using no examples and relying only on model pretraining

D. Using a few examples in a prompt to teach a model a new task

Solution

1. Step 1: Understand few-shot learning concept

   Few-shot learning means teaching a model with only a few examples instead of large datasets.

2. Step 2: Identify the role of prompts

   Prompts provide examples and instructions to guide the model on the new task.

3. Final Answer:

   Using a few examples in a prompt to teach a model a new task -> Option D

4. Quick Check:

   Few-shot learning = few examples + prompt [OK]

Hint: Few-shot means few examples in prompt, not full training [OK]

Common Mistakes:

• Confusing few-shot with full training
• Thinking no examples are used
• Mixing few-shot with fine-tuning

2. Which of the following is the correct way to format a few-shot prompt for sentiment analysis?

easy

A. "Sentiment: Positive Review: I love this movie!\nSentiment: Negative Review: This was bad.\nSentiment: Neutral Review: The plot was..."

B. "Review: I love this movie! Sentiment: Positive\nReview: This was bad. Sentiment: Negative\nReview: The plot was..."

C. "I love this movie! Positive\nThis was bad. Negative\nThe plot was... Neutral"

D. "Review: Positive I love this movie!\nReview: Negative This was bad.\nReview: Neutral The plot was..."

Solution

1. Step 1: Check prompt structure for clarity

   The prompt should clearly separate examples with labels and inputs, like 'Review: ... Sentiment: ...'.

2. Step 2: Identify correct example formatting

   "Review: I love this movie! Sentiment: Positive\nReview: This was bad. Sentiment: Negative\nReview: The plot was..." uses a consistent pattern: input first, then label, separated by new lines.

3. Final Answer:

   "Review: I love this movie! Sentiment: Positive\nReview: This was bad. Sentiment: Negative\nReview: The plot was..." -> Option B

4. Quick Check:

   Clear input-label pairs = "Review: I love this movie! Sentiment: Positive\nReview: This was bad. Sentiment: Negative\nReview: The plot was..." [OK]

Hint: Use clear input-label pairs separated by new lines [OK]

Common Mistakes:

• Mixing label before input
• Not separating examples clearly
• Omitting labels or inputs

3. Given this few-shot prompt for a model:

"Translate English to French:\nEnglish: Hello\nFrench: Bonjour\nEnglish: Thank you\nFrench: Merci\nEnglish: Good night\nFrench:"

What is the most likely model output?

medium

A. Bonne nuit

B. Bonsoir

C. Salut

D. Au revoir

Solution

1. Step 1: Understand prompt pattern

   The prompt shows English phrases with their French translations, ending with English: Good night and expecting French output.

2. Step 2: Recall correct French translation

   The French for "Good night" is "Bonne nuit", matching the pattern of direct translation.

3. Final Answer:

   Bonne nuit -> Option A

4. Quick Check:

   Good night = Bonne nuit [OK]

Hint: Match prompt pattern and translate phrase literally [OK]

Common Mistakes:

• Choosing a greeting instead of night farewell
• Confusing similar French words
• Ignoring prompt pattern

4. You wrote this few-shot prompt for a model to classify animals:

"Animal: Dog\nType: Mammal\nAnimal: Eagle\nType: Bird\nAnimal: Salmon\nType:"

The model outputs "Dog" instead of the expected animal type. What is the likely error?

medium

A. The prompt repeats the animal name instead of type

B. The model was not given any examples

C. The prompt lacks clear separation between examples

D. The model cannot handle few-shot prompts

Solution

1. Step 1: Analyze prompt structure

   The prompt shows pairs of Animal and Type, but the last line expects the type for Salmon.

2. Step 2: Identify output mismatch

   The model outputs "Dog" (an animal) instead of a type, likely because the prompt repeats animal names where types should be.

3. Final Answer:

   The prompt repeats the animal name instead of type -> Option A

4. Quick Check:

   Output animal instead of type = prompt error [OK]

Hint: Check if prompt asks for correct label, not repeated input [OK]

Common Mistakes:

• Assuming model can't handle few-shot
• Ignoring prompt formatting errors
• Thinking examples are missing

5. You want to build a few-shot prompt to classify movie reviews as Positive or Negative. Which approach will most likely improve model accuracy?

hard

A. Use only one example of a positive review to keep prompt short

B. Avoid giving any examples and rely on model knowledge

C. Include clear examples with both positive and negative reviews and their labels

D. Add unrelated text to make prompt longer

Solution

1. Step 1: Understand few-shot prompt impact

   Providing clear examples of both classes helps the model learn the task better.

2. Step 2: Evaluate options for accuracy

   Include clear examples with both positive and negative reviews and their labels gives balanced examples, improving accuracy; others reduce clarity or add noise.

3. Final Answer:

   Include clear examples with both positive and negative reviews and their labels -> Option C

4. Quick Check:

   Balanced examples = better accuracy [OK]

Hint: Use clear, balanced examples for best few-shot results [OK]

Common Mistakes:

• Using too few or unbalanced examples
• Relying on no examples
• Adding irrelevant information