Is Synthetic Data The Future of AI? (And How To Make Your Own)

Hello everybody, Adam LK here, and today we're going to be talking about synthetic data in the modern era of artificial intelligence and large language models. Over the last couple of months, I have started to see a little bit more importance being given towards using synthetic data and generating synthetic data both with language models and for the fine-tuning or optimization of language models.

Meta's recently released LLaMA 3.1, a 45 billion parameter model, explicitly states that the new model will enable the community to unlock new workflows such as synthetic data generation and model distillation. Even the model itself was fine-tuned and optimized using synthetic data as well. This trend has even been picked up by analysts like Gartner, who estimate that by 2030, synthetic data will completely overshadow real data in AI models. Therefore, it’s essential to highlight this paradigm shift and explain why and how synthetic data is being used with large language models recently.

What is Synthetic Data?

Synthetic data has been used in various machine learning applications to augment or replace real data, improve AI models, protect sensitive data, or mitigate biases. With the scale of these language models, we are beginning to run out of good usable data. The paper "Will We Run Out of Data?" estimates that we've got around (10^(14)) or (10^(15)) publicly available tokens usable for training large language models. However, these models are starting to run into limitations due to lack of quality data, including duplicated data, inaccuracies, biases, and low entropy data.

Quality vs. Quantity

Projects like Fine Web, which is a cleaned and deduplicated version of Common Crawl including 15 trillion tokens, have been optimized for language model performance. Smaller models like Google's Gemma 2 9B have shown improved performance with quality-filtered datasets over larger datasets packed with inconsistencies. However, synthetic data is further pushing the performance of language models.

Why Use Synthetic Data?

According to the Institute of Electrical and Electronics Engineers, the need for synthetic data arises from the limitations of general-purpose LLMs in specialized and private domains. Although these models perform well in generalized environments, they sometimes lack the specialized touch necessary for domain-specific tasks.

For instance, Alpha Geometry 2, which recently scored silver in the International Math Olympics, was trained using synthetic data to improve its performance in math problem-solving.

Generating Synthetic Data with Language Models

Generative AI systems can create synthetic data thanks to the realistic text generation capabilities of foundation models. Researchers leverage these capabilities to create synthetic data for training smaller models or specialized tasks. Microsoft’s Orca and IBM’s Merlinite 7B are notable examples: they used GPT-4 and Mistral 7B respectively to generate synthetic data, which refined their performances on specific tasks.

Use Cases Beyond Language Models

Sensitive Data Usage: Synthetic data can replace sensitive data, such as financial records or healthcare data, for training models.
Data Augmentation: It can expand datasets for novel or rare tasks where data is scarce.
Mitigating Biases: It addresses under-sampling and human-induced biases found in large corpuses like Common Crawl.
Regulatory Compliance: Synthetic data helps adhere to various privacy and copyright regulations.

How to Generate Your Synthetic Data

Using tools like LangChain, you can generate specific synthetic datasets. Here’s how:

Define Your Data Model: Use a pydantic.BaseModel to structure your data attributes.
Provide Few-Shot Examples: Employ few-shot prompting by creating detailed examples to guide the data generation process.
Setup Prompts and Generate Data: Utilize LangChain's create_data_generator method with parameters like schema, language model, temperature, and prompt template to produce synthetic data.
Save Data: Export your generated data in required formats, such as CSV files.

This process allows you to produce synthetic data for various domains, from employee records to IoT device data and medical records.

Keywords

Synthetic Data
Large Language Models
Fine-tuning
Meta LLaMA 3.1
Data Augmentation
Bias Mitigation
Regulatory Compliance

FAQ

What is synthetic data? Synthetic data is information generated by a computer to either augment or replace real data for different applications, including AI models, data privacy, and bias mitigation.

Why is synthetic data becoming more important? As we run out of high-quality data for large language models, synthetic data helps ensure continued model improvement and addresses specific domain needs.

How is synthetic data generated? It is often generated using advanced AI systems and language models like GPT-4 to create realistic data that can be used for training specialized models.

What are some use cases for synthetic data? Synthetic data is utilized for enhancing AI performance, protecting sensitive information, expanding rare data sets, addressing biases, and complying with regulatory standards.

How can I generate my synthetic data? You can use tools like LangChain to define your data model, create few-shot examples, set up prompts, generate synthetic data, and save it in useful formats like CSV.

By now, you should have a comprehensive understanding of synthetic data, its importance, and steps to generate your synthetic datasets effectively. If you enjoyed this, make sure to drop a like, subscribe for more, and leave any questions in the comments below. Thank you!