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

• URL: http://arxiv.org/abs/2305.12392v3
• Date: Thu, 30 May 2024 13:23:24 GMT
• Title: PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs
• Authors: Jiuzhou Han, Nigel Collier, Wray Buntine, Ehsan Shareghi,
• Abstract summary: We show how a small language model could be trained to act as a verifier module for the output of an large language model. 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.
• Score: 28.33598529903845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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