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

Understand

Large language models (LLMs) have great potential for synthetic data generation.

• This work shows that useful data can be synthetically generated even for tasks that cannot be solved directly by LLMs: for problems with structured outputs, it is possible to prompt an LLM to perform the task in the reverse direction, by generating plausible input text for a target output structure.
• Leveraging this asymmetry in task difficulty makes it possible to produce large-scale, high-quality data for complex tasks.
• We demonstrate the effectiveness of this approach on closed information extraction, where collecting ground-truth data is challenging, and no satisfactory dataset exists to date.