Summary of Ontology-free General-domain Knowledge Graph-to-text Generation Dataset Synthesis Using Large Language Model, by Daehee Kim et al.
Ontology-Free General-Domain Knowledge Graph-to-Text Generation Dataset Synthesis using Large Language Model
by Daehee Kim, Deokhyung Kang, Sangwon Ryu, Gary Geunbae Lee
First submitted to arxiv on: 11 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent advancement in Pretrained Language Models (PLMs) has improved Knowledge Graph-to-Text (G2T) performance, but it relies on datasets with precise graph-text alignment. To address this limitation, a novel method is introduced to generate a large-scale G2T dataset called Wikipedia Ontology-Free Graph-text dataset (WikiOFGraph). This dataset contains 5.85M general-domain graph-text pairs and offers high graph-text consistency without relying on external ontologies. The experimental results show that PLMs fine-tuned on WikiOFGraph outperform those trained on other datasets across various evaluation metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to turn knowledge graphs into natural language text has been developed. This method uses a large language model and data from Wikipedia to create a big dataset of graph-text pairs. The dataset, called WikiOFGraph, is special because it doesn’t rely on external rules or dictionaries to understand the relationship between the graphs and text. Instead, it uses patterns learned from the large language model. The results show that this method can generate high-quality text summaries of knowledge graphs more accurately than previous methods. |
Keywords
» Artificial intelligence » Alignment » Knowledge graph » Large language model
