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

arXiv: https://arxiv.org/abs/2307.07312 · HTML: https://mlsanity.com/paper/2307.07312/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 61 references that are in the map.
- First posted on arXiv 14 Jul 2023.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning](https://mlsanity.com/paper/2304.05613/) (2023)
- [The Design and Implementation of XiaoIce, an Empathetic Social Chatbot](https://mlsanity.com/paper/1812.08989/) (2018)
- [DART: Open-Domain Structured Data Record to Text Generation](https://mlsanity.com/paper/2007.02871/) (2020)
- [Text Generation from Knowledge Graphs with Graph Transformers](https://mlsanity.com/paper/1904.02342/) (2019)
- [Text-to-Text Pre-Training for Data-to-Text Tasks](https://mlsanity.com/paper/2005.10433/) (2020)
- [JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs](https://mlsanity.com/paper/2106.10502/) (2021)
- [Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models](https://mlsanity.com/paper/2106.01623/) (2021)
- [Enhancing AMR-to-Text Generation with Dual Graph Representations](https://mlsanity.com/paper/1909.00352/) (2019)
- [Structural Information Preserving for Graph-to-Text Generation](https://mlsanity.com/paper/2102.06749/) (2021)
- [EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation](https://mlsanity.com/paper/2111.00276/) (2021)

## Built on by (most cited first, 24 of 24)

- [Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs](https://mlsanity.com/paper/2410.23875/) (2024)
- [Making Large Language Models Perform Better in Knowledge Graph Completion](https://mlsanity.com/paper/2310.06671/) (2023)
- [Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking using Knowledge Graphs](https://mlsanity.com/paper/2401.14640/) (2024)
- [Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generation](https://mlsanity.com/paper/2401.10186/) (2024)
- [SelfPrompt: Autonomously Evaluating LLM Robustness via Domain-Constrained Knowledge Guidelines and Refined Adversarial Prompts](https://mlsanity.com/paper/2412.00765/) (2024)
- [Knowledge Engineering using Large Language Models](https://mlsanity.com/paper/2310.00637/) (2023)
- [Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance](https://mlsanity.com/paper/2310.02107/) (2023)
- [Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models](https://mlsanity.com/paper/2310.05628/) (2023)
- [Systematic Assessment of Factual Knowledge in Large Language Models](https://mlsanity.com/paper/2310.11638/) (2023)
- [Prompting for Numerical Sequences: A Case Study on Market Comment Generation](https://mlsanity.com/paper/2404.02466/) (2024)
- [Understanding Privacy Risks of Embeddings Induced by Large Language Models](https://mlsanity.com/paper/2404.16587/) (2024)
- [KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphs](https://mlsanity.com/paper/2406.10802/) (2024)
- [ProgRAG: Hallucination-Resistant Progressive Retrieval and Reasoning over Knowledge Graphs](https://mlsanity.com/paper/2511.10240/) (2025)
- [Assertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanations](https://mlsanity.com/paper/2312.03122/) (2023)
- [FreshTab: Sourcing Fresh Data for Table-to-Text Generation Evaluation](https://mlsanity.com/paper/2510.13598/) (2025)
- [Compositional Generalization for Data-to-Text Generation](https://mlsanity.com/paper/2312.02748/) (2023)
- [Inroads to a Structured Data Natural Language Bijection and the role of LLM annotation](https://mlsanity.com/paper/2401.07190/) (2024)
- [TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copy](https://mlsanity.com/paper/2402.01714/) (2024)
- [JaccDiv: A Metric and Benchmark for Quantifying Diversity of Generated Marketing Text in the Music Industry](https://mlsanity.com/paper/2504.20849/) (2025)
- [SNAPE-PM: Building and Utilizing Dynamic Partner Models for Adaptive Explanation Generation](https://mlsanity.com/paper/2505.13053/) (2025)
- [Leveraging Knowledge Graphs and LLMs for Structured Generation of Misinformation](https://mlsanity.com/paper/2505.24479/) (2025)
- [LLM Agents Implement an NLG System from Scratch: Building Interpretable Rule-Based RDF-to-Text Generators](https://mlsanity.com/paper/2512.18360/) (2025)
- [Compositional Chain-of-Relations for Faithful Knowledge Graph Question Answering with Large Language Models](https://mlsanity.com/paper/2608.22762/) (2026)
- [Do LLMs Make More Mistakes If They Do Not Believe the Input Data?](https://mlsanity.com/paper/2609.09363/) (2026)

## Abstract (arXiv)

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.