  Large language models (LLMs) have shown great abilities of solving various
natural language tasks in different domains. Due to the training objective of
LLMs and their pre-training data, LLMs are not very well equipped for tasks
involving structured data generation. We propose a framework, Prompting with
Iterative Verification (PiVe), to improve graph-based generative capability of
LLMs. We show how a small language model could be trained to act as a verifier
module for the output of an LLM~(i.e., ChatGPT, GPT-4), and to iteratively
improve its performance via fine-grained corrective instructions. We also show
how the verifier module could apply iterative corrections offline for a more
cost-effective solution to the text-to-graph generation task. Experiments on
three graph-based datasets show consistent improvement gained via PiVe.
Additionally, we create GenWiki-HIQ and highlight that the verifier module can
be used as a data augmentation tool to help improve the quality of
automatically generated parallel text-graph datasets.
