Synthetic data set creation involves generating artificial data that can mimic real-world scenarios without relying on sensitive or limited-access data sources. While synthetic data for structured data sets has been explored extensively (e.g., for performance testing or privacy-safe environments), we're seeing renewed use of synthetic data for unstructured data. Enterprises often struggle with a lack of labeled domain-specific data, especially for use in training or fine-tuning LLMs. Tools like Bonito and Microsoft's AgentInstruct can generate synthetic instruction-tuning data from raw sources such as text documents and code files. This helps accelerate model training while reducing costs and dependency on manual data curation. Another important use case is generating synthetic data to address imbalanced or sparse data, which is common in tasks like fraud detection or customer segmentation. Techniques such as SMOTE help balance data sets by artificially creating minority class instances. Similarly, in industries like finance, generative adversarial networks (GANs) are used to simulate rare transactions, allowing models to be robust in detecting edge cases and improving overall performance.
