Cohere for AI Enhances Large Language Models LLMs with Active Inheritance: Steering Synthetic Data Generation for Optimal Performance and Reduced Bias
How does targeted sampling influence synthetic data characteristics?
Targeted sampling influences synthetic data characteristics by intentionally selecting specific individuals or groups based on certain criteria, allowing researchers to focus on particular segments of a population and obtain more relevant and valuable data3. This method enables the steering of synthetic data generation towards desired non-differentiable objectives, such as high lexical diversity and low toxicity, resulting in substantial improvements in model performance and reduced biases.
What is synthetic data generation in machine learning?
Synthetic data generation in machine learning refers to the process of creating artificial data that mimics real-world data patterns using algorithms or simulations6. This technique is used when real-world data is limited, expensive, or sensitive, allowing researchers to train machine learning models more effectively and enhance their performance across various applications.
How does synthetic data enhance machine learning model performance?
Synthetic data enhances machine learning model performance by providing more data for training and addressing issues of minority classes5. This can lead to a performance increase of up to 15%, depending on the dataset and model5. Additionally, synthetic data can improve fairness and explainability in machine learning models.