Enhancing Language Model Fine-tuning with LLM Data Augmentation

MonsterAPI introduces a new Data Augmentation API to streamline the process of augmenting and scaling out datasets for fine-tuning large language models (LLMs). Data augmentation involves artificially expanding a dataset by creating modified versions of existing data points, which helps improve model performance without the need for manual data collection and wrangling efforts. The role of data augmentation in fine-tuning LLMs includes increasing dataset size, making models more robust, and improving data quality. MonsterAPI's Data Augmentation API supports two kinds of data augmentation: Evol-Instruct and Ultrafeedback. A case study demonstrates the benefits of data augmentation by showing how it can enhance model performance in domain-specific applications where data may be scarce.