Efficient Embedding-based Synthetic Data Generation for Complex Reasoning Tasks
This work addresses a key bottleneck in synthetic data generation for fine-tuning smaller, more efficient LLMs, offering an incremental improvement in data diversity and model performance.
The paper tackles the challenge of ensuring quality and diversity in synthetic data generation for complex reasoning tasks by analyzing embedding space distributions, showing a correlation between example density and prediction accuracy, and proposes a targeted embedding-based sampling pipeline that improves performance across benchmarks.
Synthetic Data Generation (SDG), leveraging Large Language Models (LLMs), has recently been recognized and broadly adopted as an effective approach to improve the performance of smaller but more resource and compute efficient LLMs through fine-tuning. A key challenge in SDG is ensuring the quality and diversity of the generated data. In this paper, we analyze the diversity and distribution of generated data in the embedding space, and demonstrate a strong correlation between the density of examples within a specific neighborhood and the accuracy of predictions on examples drawn from that region. Building on this insight, we present a targeted pipeline for embedding-based sampling that enhances data diversity and consistently improves performance across several benchmarks.