Generating Synthetic Data with LLMs

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Introduction

In this article, we will delve into the intricate process of synthetic data generation using LLMs. We will shed light on the concept behind the increasing importance of synthetic data, the prowess of LLMs in generating such data, and practical steps to harness the power of advanced models like OpenAI’s GPT-3.5.

 Whether you’re a seasoned AI enthusiast or a curious newcomer, embark with us on this enlightening journey into the heart of modern machine learning.

What are LLMs?

Large Language Models (LLMs) are state-of-the-art machine learning architectures primarily designed for understanding and generating human-like text. These models are trained on vast amounts of data, enabling them to perform a wide range of language tasks, from simple text completion to answering complex questions or even crafting coherent articles. Some examples of LLMs include:

1. GPT-3 by OpenAI, with 175 billion parameters and up to 2048 tokens per unit.

2. BERT by Google, with 340 million parameters and up to 512 tokens per unit.

3. T5 (Text-to-Text Transfer Transformer by Google) with parameters ranging from 60 million to 11 billion depending on the model size. The number of tokens it can process is also influenced by its size and setup.

That being said, LLMs, with their cutting-edge capabilities in NLP tasks like question answering and text summarization, are also highly regarded for their efficiency in generating synthetic data.

Why Is There A Need for Synthetic Data

1) Data Scarcity

Do you ever grapple with the challenge of insufficient data to train your model? This dilemma is a daily reality for machine learning experts globally. Given that data gathering and processing are among the most daunting aspects of the entire machine-learning journey, the significance of synthetic data cannot be overstated.

2) Data Privacy & Security

Real-world data often contains sensitive information. For industries like healthcare and finance, there are stringent regulations around data usage. Such data may include customer’s credit cards, buying patterns, and diseases. Synthetic data can be used without compromising privacy since it doesn't contain real individual information.

The Process of Generating Data with LLMs

The journey of producing synthetic data using Large Language Models begins with the preparation of seed data or guiding queries. This foundational step is paramount as it sets the trajectory for the type of synthetic data one wishes to produce. Whether it's simulating chatbot conversations or creating fictional product reviews, these initial prompts provide LLMs with the necessary context.

Once the stage is set, we delve into the actual data generation phase. LLMs, with their advanced architectures, begin crafting text based on patterns they've learned from vast datasets. This capability enables them to produce information that aligns with the characteristics of real-world data, albeit synthesized.

Generating Synthetic Data Using OpenAI’s GPT 3.5

Step 1: Importing Neseccasry Libraries

import openai

Step 2: Set up the OpenAI API key

openai.api_key = "Insert Your OpenAI key here"

Step 3: Define our synthetic data generation function

def generate_reviews(prompt, count=1):
    reviews = []

    for i in range(count):
        review_generated = False
        while not review_generated:
            try:
                # Generate a response using the ChatCompletion method
                response = openai.ChatCompletion.create(
                    model="gpt-3.5-turbo",
                    messages=[
                        {"role": "system", "content": "You are a helpful assistant."},
                        {"role": "user", "content": prompt}
                    ]
                )
               
                review = response.choices[0].message['content'].strip()
                word_count = len(review.split())
                print("word count:", word_count)

                # Check if the word count is within the desired range
                if 15 <= word_count <= 70:
                    print("counted")
                    reviews.append(review)
                    review_generated = True

            except openai.error.OpenAIError as err:
                print(f"Encountered an error: {err}")

        # Optional: Add a slight variation to the prompt for next iteration
        prompt += " Provide another perspective."

    return reviews

Step 4: Testing our function

prompt_text = "Write a 25 word positive review for a wireless earbud highlighting its battery life."
num_datapoints = 5
generated_reviews = generate_reviews(prompt_text, num_datapoints)

Step 5: Printing generated synthetic data

for idx, review in enumerate(generated_reviews):
    print(f"Review {idx + 1}: {review}")

Output:

Review 1: The battery life on these wireless earbuds is absolutely incredible! I can enjoy hours of uninterrupted music without worrying about recharging. Truly impressive!

Review 2: "The battery life of these wireless earbuds is phenomenal! I can enjoy my favorite music for hours without worrying about recharging. Truly impressive!"

Review 3: This wireless earbud is a game-changer! With an exceptional battery life that lasts all day, I can enjoy uninterrupted music and calls without any worries. It's a must-have for people on the go. Another perspective: As a fitness enthusiast, the long battery life of this wireless earbud is a true blessing. It allows me to power through my workouts without constantly needing to recharge, keeping me focused and motivated.

Review 4: This wireless earbud's exceptional battery life is worth praising! It lasts all day long, keeping you immersed in your favorite tunes. A real game-changer for music enthusiasts.

Review 5: The battery life of these wireless earbuds is exceptional, lasting for hours on end, allowing you to enjoy uninterrupted music or calls. They truly exceed expectations!

Considerations and Pitfalls

However, the process doesn't conclude here. Generated data may sometimes have inconsistencies or lack the desired quality. Hence, post-processing, which involves refining and filtering the output, becomes essential. Furthermore, ensuring the variability and richness of the synthetic data is paramount, as too much uniformity can lead to overfitting when the data is employed for machine learning purposes. This refinement process should aim to eliminate any redundant or unrepresentative samples that could skew the model's learning process.

Moreover, validating the synthetic data ensures that it meets the standards and purposes for which it was intended, ensuring both authenticity and reliability.

Conclusion

Throughout this article, we've navigated the process of synthetic data generation powered by LLMs. We've explained the underlying reasons for the escalating prominence of synthetic data, showcased the unparalleled proficiency of LLMs in creating such data, and provided actionable guidance to leverage the capabilities of pre-trained LLM models like OpenAI’s GPT-3.5.

For all AI enthusiasts, we hope this exploration has deepened your appreciation and understanding of the evolving tapestry of machine learning,  LLMs, and synthetic data. As we stand now, it is clear that both synthetic data and LLMs will be central to many breakthroughs to come.

Author Bio

Mostafa Ibrahim is a dedicated software engineer based in London, where he works in the dynamic field of Fintech. His professional journey is driven by a passion for cutting-edge technologies, particularly in the realms of machine learning and bioinformatics. When he's not immersed in coding or data analysis, Mostafa loves to travel.

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