Creating and Validating Synthetic Datasets for LLM Evaluation & Experimentation

Synthetic datasets are artificially created data sources that mimic real-world information for use in large language model (LLM) evaluation and experimentation. They offer several advantages, including controlled environments for testing, coverage of edge cases, and protection of user privacy by avoiding the use of actual data. These datasets can be used to test and validate model performance, generate initial traces of application behavior, and serve as "golden data" for consistent experimental results. Creating synthetic datasets involves defining objectives, choosing data sources, generating data using automated or rule-based methods, and ensuring diversity and representativeness in the data. Validation is crucial to ensure accurate representation of patterns and distributions found in actual use cases. Combining synthetic datasets with human evaluation can improve their overall quality and effectiveness. Best practices for synthetic dataset use include implementing a regular refresh cycle, maintaining transparency in data generation processes, regularly evaluating dataset performance against real-world data and newer models, and taking a balanced approach when augmenting synthetic datasets with human-curated examples. By following these guidelines and staying up to date with emerging research and best practices, developers can maximize the long-term value and reliability of their synthetic datasets for LLM evaluation and experimentation.