Evaluating Language Models as Synthetic Data Generators

Abstract

Given the increasing use of synthetic data in language model (LM) post-training, an LM's ability to generate high-quality data has become nearly as crucial as its ability to solve problems directly. While prior works have focused on developing effective data generation methods, they lack systematic comparison of different LMs as data generators in a unified setting.

To address this gap, we propose AgoraBench, a benchmark that provides standardized settings and metrics to evaluate LMs' data generation abilities. Through synthesizing 1.26 million training instances using 6 LMs and training 99 student models, we uncover key insights about LMs' data generation capabilities. We find that LMs exhibit distinct strengths (e.g., GPT-4o excels at generating new problems, while Claude 3.5 Sonnet performs better at enhancing existing ones) and that an LM's data generation ability does not necessarily correlate with its problem-solving ability.