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# Few-Shot Learning Guide
## Overview
Few-shot learning enables LLMs to perform tasks by providing a small number of examples (typically 1-10) within the prompt. This technique is highly effective for tasks requiring specific formats, styles, or domain knowledge.
## Example Selection Strategies
### 1. Semantic Similarity
Select examples most similar to the input query using embedding-based retrieval.
```python
from sentence_transformers import SentenceTransformer
import numpy as np
class SemanticExampleSelector:
def __init__(self, examples, model_name='all-MiniLM-L6-v2'):
self.model = SentenceTransformer(model_name)
self.examples = examples
self.example_embeddings = self.model.encode([ex['input'] for ex in examples])
def select(self, query, k=3):
query_embedding = self.model.encode([query])
similarities = np.dot(self.example_embeddings, query_embedding.T).flatten()
top_indices = np.argsort(similarities)[-k:][::-1]
return [self.examples[i] for i in top_indices]
```
**Best For**: Question answering, text classification, extraction tasks
### 2. Diversity Sampling
Maximize coverage of different patterns and edge cases.
```python
from sklearn.cluster import KMeans
class DiversityExampleSelector:
def __init__(self, examples, model_name='all-MiniLM-L6-v2'):
self.model = SentenceTransformer(model_name)
self.examples = examples
self.embeddings = self.model.encode([ex['input'] for ex in examples])
def select(self, k=5):
# Use k-means to find diverse cluster centers
kmeans = KMeans(n_clusters=k, random_state=42)
kmeans.fit(self.embeddings)
# Select example closest to each cluster center
diverse_examples = []
for center in kmeans.cluster_centers_:
distances = np.linalg.norm(self.embeddings - center, axis=1)
closest_idx = np.argmin(distances)
diverse_examples.append(self.examples[closest_idx])
return diverse_examples
```
**Best For**: Demonstrating task variability, edge case handling
### 3. Difficulty-Based Selection
Gradually increase example complexity to scaffold learning.
```python
class ProgressiveExampleSelector:
def __init__(self, examples):
# Examples should have 'difficulty' scores (0-1)
self.examples = sorted(examples, key=lambda x: x['difficulty'])
def select(self, k=3):
# Select examples with linearly increasing difficulty
step = len(self.examples) // k
return [self.examples[i * step] for i in range(k)]
```
**Best For**: Complex reasoning tasks, code generation
### 4. Error-Based Selection
Include examples that address common failure modes.
```python
class ErrorGuidedSelector:
def __init__(self, examples, error_patterns):
self.examples = examples
self.error_patterns = error_patterns # Common mistakes to avoid
def select(self, query, k=3):
# Select examples demonstrating correct handling of error patterns
selected = []
for pattern in self.error_patterns[:k]:
matching = [ex for ex in self.examples if pattern in ex['demonstrates']]
if matching:
selected.append(matching[0])
return selected
```
**Best For**: Tasks with known failure patterns, safety-critical applications
## Example Construction Best Practices
### Format Consistency
All examples should follow identical formatting:
```python
# Good: Consistent format
examples = [
{
"input": "What is the capital of France?",
"output": "Paris"
},
{
"input": "What is the capital of Germany?",
"output": "Berlin"
}
]
# Bad: Inconsistent format
examples = [
"Q: What is the capital of France? A: Paris",
{"question": "What is the capital of Germany?", "answer": "Berlin"}
]
```
### Input-Output Alignment
Ensure examples demonstrate the exact task you want the model to perform:
```python
# Good: Clear input-output relationship
example = {
"input": "Sentiment: The movie was terrible and boring.",
"output": "Negative"
}
# Bad: Ambiguous relationship
example = {
"input": "The movie was terrible and boring.",
"output": "This review expresses negative sentiment toward the film."
}
```
### Complexity Balance
Include examples spanning the expected difficulty range:
```python
examples = [
# Simple case
{"input": "2 + 2", "output": "4"},
# Moderate case
{"input": "15 * 3 + 8", "output": "53"},
# Complex case
{"input": "(12 + 8) * 3 - 15 / 5", "output": "57"}
]
```
## Context Window Management
### Token Budget Allocation
Typical distribution for a 4K context window:
```
System Prompt: 500 tokens (12%)
Few-Shot Examples: 1500 tokens (38%)
User Input: 500 tokens (12%)
Response: 1500 tokens (38%)
```
### Dynamic Example Truncation
```python
class TokenAwareSelector:
def __init__(self, examples, tokenizer, max_tokens=1500):
self.examples = examples
self.tokenizer = tokenizer
self.max_tokens = max_tokens
def select(self, query, k=5):
selected = []
total_tokens = 0
# Start with most relevant examples
candidates = self.rank_by_relevance(query)
for example in candidates[:k]:
example_tokens = len(self.tokenizer.encode(
f"Input: {example['input']}\nOutput: {example['output']}\n\n"
))
if total_tokens + example_tokens <= self.max_tokens:
selected.append(example)
total_tokens += example_tokens
else:
break
return selected
```
## Edge Case Handling
### Include Boundary Examples
```python
edge_case_examples = [
# Empty input
{"input": "", "output": "Please provide input text."},
# Very long input (truncated in example)
{"input": "..." + "word " * 1000, "output": "Input exceeds maximum length."},
# Ambiguous input
{"input": "bank", "output": "Ambiguous: Could refer to financial institution or river bank."},
# Invalid input
{"input": "!@#$%", "output": "Invalid input format. Please provide valid text."}
]
```
## Few-Shot Prompt Templates
### Classification Template
```python
def build_classification_prompt(examples, query, labels):
prompt = f"Classify the text into one of these categories: {', '.join(labels)}\n\n"
for ex in examples:
prompt += f"Text: {ex['input']}\nCategory: {ex['output']}\n\n"
prompt += f"Text: {query}\nCategory:"
return prompt
```
### Extraction Template
```python
def build_extraction_prompt(examples, query):
prompt = "Extract structured information from the text.\n\n"
for ex in examples:
prompt += f"Text: {ex['input']}\nExtracted: {json.dumps(ex['output'])}\n\n"
prompt += f"Text: {query}\nExtracted:"
return prompt
```
### Transformation Template
```python
def build_transformation_prompt(examples, query):
prompt = "Transform the input according to the pattern shown in examples.\n\n"
for ex in examples:
prompt += f"Input: {ex['input']}\nOutput: {ex['output']}\n\n"
prompt += f"Input: {query}\nOutput:"
return prompt
```
## Evaluation and Optimization
### Example Quality Metrics
```python
def evaluate_example_quality(example, validation_set):
metrics = {
'clarity': rate_clarity(example), # 0-1 score
'representativeness': calculate_similarity_to_validation(example, validation_set),
'difficulty': estimate_difficulty(example),
'uniqueness': calculate_uniqueness(example, other_examples)
}
return metrics
```
### A/B Testing Example Sets
```python
class ExampleSetTester:
def __init__(self, llm_client):
self.client = llm_client
def compare_example_sets(self, set_a, set_b, test_queries):
results_a = self.evaluate_set(set_a, test_queries)
results_b = self.evaluate_set(set_b, test_queries)
return {
'set_a_accuracy': results_a['accuracy'],
'set_b_accuracy': results_b['accuracy'],
'winner': 'A' if results_a['accuracy'] > results_b['accuracy'] else 'B',
'improvement': abs(results_a['accuracy'] - results_b['accuracy'])
}
def evaluate_set(self, examples, test_queries):
correct = 0
for query in test_queries:
prompt = build_prompt(examples, query['input'])
response = self.client.complete(prompt)
if response == query['expected_output']:
correct += 1
return {'accuracy': correct / len(test_queries)}
```
## Advanced Techniques
### Meta-Learning (Learning to Select)
Train a small model to predict which examples will be most effective:
```python
from sklearn.ensemble import RandomForestClassifier
class LearnedExampleSelector:
def __init__(self):
self.selector_model = RandomForestClassifier()
def train(self, training_data):
# training_data: list of (query, example, success) tuples
features = []
labels = []
for query, example, success in training_data:
features.append(self.extract_features(query, example))
labels.append(1 if success else 0)
self.selector_model.fit(features, labels)
def extract_features(self, query, example):
return [
semantic_similarity(query, example['input']),
len(example['input']),
len(example['output']),
keyword_overlap(query, example['input'])
]
def select(self, query, candidates, k=3):
scores = []
for example in candidates:
features = self.extract_features(query, example)
score = self.selector_model.predict_proba([features])[0][1]
scores.append((score, example))
return [ex for _, ex in sorted(scores, reverse=True)[:k]]
```
### Adaptive Example Count
Dynamically adjust the number of examples based on task difficulty:
```python
class AdaptiveExampleSelector:
def __init__(self, examples):
self.examples = examples
def select(self, query, max_examples=5):
# Start with 1 example
for k in range(1, max_examples + 1):
selected = self.get_top_k(query, k)
# Quick confidence check (could use a lightweight model)
if self.estimated_confidence(query, selected) > 0.9:
return selected
return selected # Return max_examples if never confident enough
```
## Common Mistakes
1. **Too Many Examples**: More isn't always better; can dilute focus
2. **Irrelevant Examples**: Examples should match the target task closely
3. **Inconsistent Formatting**: Confuses the model about output format
4. **Overfitting to Examples**: Model copies example patterns too literally
5. **Ignoring Token Limits**: Running out of space for actual input/output
## Resources
- Example dataset repositories
- Pre-built example selectors for common tasks
- Evaluation frameworks for few-shot performance
- Token counting utilities for different models