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Q1. Explain the difference between Zero-shot learning, One-shot learning, and few-shot learning.

Ans:

Difference Explanation:

  • Zero-shot Learning:

    • The model is asked to perform a task on completely new and unseen data.
    • No explicit examples related to the task are provided during training.
    • The model relies on its pre-existing knowledge to generalize to the new task.

  • One-shot Learning:

    • The model is trained with only one labeled example for each class or task.
    • Requires the model to generalize effectively from a very small amount of data.

  • Few-shot Learning:

    • The model is trained with a small number of labeled examples for each class or task (more than one).
    • Provides slightly more training data compared to one-shot learning but still involves learning from a limited dataset.

Example Scenarios:

Zero-shot Learning:

Task: Summarization of an unseen document.

# Example using GPT-3 for zero-shot summarization
import openai

prompt = "Summarize the following article: [your unseen document here]"

response = openai.Completion.create(
  engine="text-davinci-002",
  prompt=prompt,
  max_tokens=150  # Adjust token limit as needed
)

summary = response.choices[0].text.strip()
print(summary)

One-shot Learning:

Task: Translation of a phrase in a language with only one training example.

# Example using GPT-3 for one-shot translation
import openai

prompt = "Translate the following phrase to French: [your phrase here]"

response = openai.Completion.create(
  engine="text-davinci-002",
  prompt=prompt,
  max_tokens=50  # Adjust token limit as needed
)

translation = response.choices[0].text.strip()
print(translation)

Few-shot Learning:

Task: Sentiment analysis with a few labeled examples.

# Example using GPT-3 for few-shot sentiment analysis
import openai

prompt = "Determine the sentiment of the following reviews:\n1. Positive: [positive review]\n2. Negative: [negative review]\n3. Neutral: [neutral review]"

response = openai.Completion.create(
  engine="text-davinci-002",
  prompt=prompt,
  max_tokens=100  # Adjust token limit as needed
)

sentiment_analysis = response.choices[0].text.strip()
print(sentiment_analysis)

In these examples, GPT-3 is leveraged for natural language understanding tasks, and the specific prompt structure guides the model on how to approach each learning scenario. Keep in mind that these examples are illustrative, and the actual implementation might involve more fine-tuning or optimization based on the specific task and dataset.