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

• In any system that uses structured knowledgegraph (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 little 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.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Språkteknologi (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Language Technology (hsv//eng)

Keyword

large language model

llm

lexicalisation

kg-to-text

data-to-text

bias

hallucination

triples

triple

knowledge graph

KG

webnlg

wikidata

språkmodell

llm

lexikalisering

data till text

kg till text

hallucination

triplett

kunskapsgraf

KG

webnlg

wikidata

Speech and Music Communication

Tal- och musikkommunikation

Publication and Content Type

ref (subject category)

kon (subject category)