Synthetic Data Generation to Improve Language Models

Synthetic Data Generation (SDG) is emerging as an effective technique to improve the performance of smaller language models that are more efficient in terms of computational resources. The approach leverages Large Language Models (LLM) to create artificial datasets for use in fine-tuning.

A crucial challenge in SDG is ensuring that the generated data is of high quality and sufficiently diverse. A new study focuses on analyzing the diversity and distribution of the generated data in the embedding space. The results indicate a strong correlation between the density of examples in a specific area and the accuracy of predictions on examples from that same region.

Based on this observation, the researchers developed a targeted pipeline for embedding-based sampling. This method aims to increase data diversity and, consequently, improve model performance on various benchmarks.

Implications for LLM Fine-tuning

The ability to generate high-quality synthetic data opens new possibilities for the development and optimization of language models. Using embeddings to guide the sampling process represents a step towards creating more effective datasets for fine-tuning.