Leveraging existing LLMs for data generation
One of the most powerful approaches to data augmentation for LLMs is to use existing models to generate new training examples. This technique, often referred to as self-supervised learning or model-based data augmentation, allows us to create vast amounts of diverse, high-quality training data.
We’ll explore how to use GPT-4o and the OpenAI API for data generation:
def gpt4o_data_generation(prompt, num_samples=5):
response = openai.ChatCompletion.create(
model="gpt-4o",
messages=[{"role": "user", "content": prompt}],
max_tokens=150,
n=num_samples,
temperature=0.7,
)
return [choice...
