Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphs

TL;DR

This paper explores using large language models like ChatGPT for zero-shot natural language generation from knowledge graphs, demonstrating near state-of-the-art results and analyzing how prior knowledge impacts output quality.

Contribution

It introduces a zero-shot KG-to-text generation approach leveraging large language models, highlighting their ability to generate coherent text without task-specific training.

Findings

ChatGPT achieves near state-of-the-art on WebNLG 2020

Performance varies across different evaluation metrics

Prior knowledge significantly influences output quality

Abstract

In any system that uses structured knowledge graph (KG) data as its underlying knowledge representation, KG-to-text generation is a useful tool for turning parts of the graph data into text that can be understood by humans. Recent work has shown that models that make use of pretraining on large amounts of text data can perform well on the KG-to-text task even with relatively small sets of training data on the specific graph-to-text task. In this paper, we build on this concept by using large language models to perform zero-shot generation based on nothing but the model's understanding of the triple structure from what it can read. We show that ChatGPT achieves near state-of-the-art performance on some measures of the WebNLG 2020 challenge, but falls behind on others. Additionally, we compare factual, counter-factual and fictional statements, and show that there is a significant connection between what the LLM already knows about the data it is parsing and the quality of the output text.