Few-shot learning with prompts in NLP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is few-shot learning in NLP?

Few-shot learning is a method where a model learns to perform a task using only a small number of examples, often just a few, instead of needing large datasets.

beginner

How do prompts help in few-shot learning?

Prompts guide the model by providing context or examples in natural language, helping it understand the task with very few examples.

intermediate

What is an example of a prompt for sentiment analysis in few-shot learning?

A prompt could be: "Review: 'I love this movie!' Sentiment: Positive" followed by "Review: 'The food was bad.' Sentiment:" where the model predicts the sentiment for the second review.

beginner

Why is few-shot learning useful in real life?

It allows models to quickly adapt to new tasks or languages without needing large labeled datasets, saving time and resources.

intermediate

What is a key challenge when using few-shot learning with prompts?

Designing effective prompts that clearly communicate the task to the model can be tricky and may require trial and error.

What does 'few-shot' mean in few-shot learning?

A Learning from a few examples

B Learning from a large dataset

C Learning without any examples

D Learning only from images

Few-shot learning means the model learns from only a few examples.

How do prompts assist a model in few-shot learning?

A By removing examples

B By providing task context and examples

C By changing the model architecture

D By increasing dataset size

Prompts give the model context and examples to understand the task better.

Which is a good example of a prompt for few-shot learning?

A "Translate: Hello -> Hola"

B "Random text without context"

C "No examples given"

D "Just a single word"

A prompt with example translations helps the model learn the task.

Why is few-shot learning important?

A It ignores task instructions

B It requires more data than usual

C It only works for images

D It reduces the need for large labeled datasets

Few-shot learning helps models learn tasks with less data.

What is a common difficulty when using prompts for few-shot learning?

A Training very large models

B Collecting millions of examples

C Creating clear and effective prompts

D Ignoring the task

Designing prompts that clearly explain the task is often challenging.

Explain how few-shot learning with prompts works in simple terms.

Think about teaching a friend with just a few examples and clear instructions.

Describe a real-life situation where few-shot learning with prompts could be helpful.

Imagine needing to teach a model a new skill quickly without lots of data.

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