Efficient Embedding-based Synthetic Data Generation for Complex Reasoning Tasks

ACL ARR 2025 May Submission4115 Authors

Abstract: 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.

Paper Type: Long

Research Area: Language Modeling

Research Area Keywords: synthetic data generation,fine-tuning,word embeddings,generative models,data augmentation

Contribution Types: Model analysis & interpretability, Data analysis

Languages Studied: English

Keywords: synthetic data generation, fine-tuning, word embeddings, generative models, data augmentation

Submission Number: 4115