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Large language models (LLMs) have great potential for synthetic datageneration. This work shows that useful data can be synthetically generatedeven for tasks that cannot be solved directly by LLMs: for problems withstructured outputs, it is possible to prompt an LLM to perform the task in thereverse direction, by generating plausible input text for a target outputstructure. Leveraging this asymmetry in task difficulty makes it possible toproduce large-scale, high-quality data for complex tasks. We demonstrate theeffectiveness of this approach on closed information extraction, wherecollecting ground-truth data is challenging, and no satisfactory dataset existsto date. We synthetically generate a dataset of 1.8M data points, establish itssuperior quality compared to existing datasets in a human evaluation, and useit to finetune small models (220M and 770M parameters), termed SynthIE, thatoutperform the prior state of the art (with equal model size) by a substantialmargin of 57 absolute points in micro-F1 and 79 points in macro-F1. Code, data,and models are available at https://github.com/epfl-dlab/SynthIE.

Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction