mlsanityData to 7 Oct 2026

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

arXiv 2307.07312 ↗First posted 14 Jul 2023cs.CLcited by 24

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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.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 17 of its 61 references that are in the map. First posted on arXiv 14 Jul 2023.

Builds on

17 of 61 references.

References · 17

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  4. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  5. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  6. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
  7. 2020Knowledge Graphscited by 619
  8. 2023ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learningcited by 203
  9. 2018The Design and Implementation of XiaoIce, an Empathetic Social Chatbotcited by 172
  10. 2020DART: Open-Domain Structured Data Record to Text Generationcited by 144
  11. 2019Text Generation from Knowledge Graphs with Graph Transformerscited by 122
  12. 2020Text-to-Text Pre-Training for Data-to-Text Taskscited by 104
5 more
  1. 2021JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphscited by 58
  2. 2021Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Modelscited by 29
  3. 2019Enhancing AMR-to-Text Generation with Dual Graph Representationscited by 24
  4. 2021Structural Information Preserving for Graph-to-Text Generationcited by 24
  5. 2021EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generationcited by 14

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 7; 2024: 9; 2025: 6; 2026: 2.

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  1. 2024Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphscited by 77
  2. 2023Making Large Language Models Perform Better in Knowledge Graph Completioncited by 48
  3. 2024Beyond Traditional Benchmarks: Analyzing Behaviors of Open LLMs on Data-to-Text Generationcited by 16
  4. 2024Can LLMs Evaluate Complex Attribution in QA? Automatic Benchmarking using Knowledge Graphscited by 16
  5. 2024SelfPrompt: Autonomously Evaluating LLM Robustness via Domain-Constrained Knowledge Guidelines and Refined Adversarial Promptscited by 10
  6. 2023Knowledge Engineering using Large Language Modelscited by 9
  7. 2023Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performancecited by 9
  8. 2023Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Modelscited by 6
  9. 2023Systematic Assessment of Factual Knowledge in Large Language Modelscited by 5
  10. 2024Prompting for Numerical Sequences: A Case Study on Market Comment Generationcited by 3
  11. 2024KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphscited by 3
  12. 2025ProgRAG: Hallucination-Resistant Progressive Retrieval and Reasoning over Knowledge Graphscited by 3
12 more
  1. 2023Assertion Enhanced Few-Shot Learning: Instructive Technique for Large Language Models to Generate Educational Explanationscited by 2
  2. 2024Understanding Privacy Risks of Embeddings Induced by Large Language Modelscited by 2
  3. 2023Compositional Generalization for Data-to-Text Generationcited by 1
  4. 2025FreshTab: Sourcing Fresh Data for Table-to-Text Generation Evaluationcited by 1
  5. 2024Inroads to a Structured Data Natural Language Bijection and the role of LLM annotationcited by 0
  6. 2024TrICy: Trigger-guided Data-to-text Generation with Intent aware Attention-Copycited by 0
  7. 2025JaccDiv: A Metric and Benchmark for Quantifying Diversity of Generated Marketing Text in the Music Industrycited by 0
  8. 2025SNAPE-PM: Building and Utilizing Dynamic Partner Models for Adaptive Explanation Generationcited by 0
  9. 2025Leveraging Knowledge Graphs and LLMs for Structured Generation of Misinformationcited by 0
  10. 2025LLM Agents Implement an NLG System from Scratch: Building Interpretable Rule-Based RDF-to-Text Generatorscited by 0
  11. 2026Compositional Chain-of-Relations for Faithful Knowledge Graph Question Answering with Large Language Modelscited by 0
  12. 2026Do LLMs Make More Mistakes If They Do Not Believe the Input Data?cited by 0