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Large language models (LLMs) have shown great abilities of solving variousnatural language tasks in different domains. Due to the training objective ofLLMs and their pretraining data, LLMs are not very well equipped for tasksinvolving structured data generation. We propose a framework, Prompting withIterative Verification (PiVe), to improve graphbased generative capability ofLLMs. We show how a small language model could be trained to act as a verifiermodule for the output of an LLM (i.e., ChatGPT), and to iteratively improve itsperformance via fine-grained corrective instructions. Additionally, we show howthe verifier module could apply iterative corrections offline for a morecost-effective solution to the text-to-graph generation task. Experiments onthree graph-based datasets show consistent improvement gained via PiVe.Additionally, we highlight how the proposed verifier module can be used as adata augmentation tool to help improve the quality of automatically generatedparallel text-graph datasets. Our code and data are available athttps://github.com/Jiuzhouh/PiVe.

PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs