mlsanityData to 7 Oct 2026

Few-shot Knowledge Graph-to-Text Generation with Pretrained Language Models

arXiv 2106.01623 ↗First posted 3 Jun 2021cs.CLcited by 29

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Abstract (arXiv)

This paper studies how to automatically generate a natural language text that describes the facts in knowledge graph (KG). Considering the few-shot setting, we leverage the excellent capacities of pretrained language models (PLMs) in language understanding and generation. We make three major technical contributions, namely representation alignment for bridging the semantic gap between KG encodings and PLMs, relation-biased KG linearization for deriving better input representations, and multi-task learning for learning the correspondence between KG and text. Extensive experiments on three benchmark datasets have demonstrated the effectiveness of our model on KG-to-text generation task. In particular, our model outperforms all comparison methods on both fully-supervised and few-shot settings. Our code and datasets are available at https://github.com/RUCAIBox/Few-Shot-KG2Text.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). Builds on 22 of its 39 references that are in the map. First posted on arXiv 3 Jun 2021.

Builds on

22 of 39 references.

References · 22

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  4. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  5. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  6. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,027
  7. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  8. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,281
  9. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  10. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  11. 2019ERNIE: Enhanced Language Representation with Informative Entitiescited by 525
  12. 2019Knowledge Enhanced Contextual Word Representationscited by 294
10 more
  1. 2019Text Generation from Knowledge Graphs with Graph Transformerscited by 122
  2. 2020Investigating Pretrained Language Models for Graph-to-Text Generationcited by 117
  3. 2021Pretrained Language Models for Text Generation: A Surveycited by 95
  4. 2019Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generationcited by 75
  5. 2019Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modelingcited by 71
  6. 2019Few-Shot NLG with Pre-Trained Language Modelcited by 52
  7. 2020CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Trainingcited by 38
  8. 2020Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphscited by 30
  9. 2021Structural Information Preserving for Graph-to-Text Generationcited by 24
  10. 2021TextBox: A Unified, Modularized, and Extensible Framework for Text Generationcited by 15

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 9; 2024: 8; 2025: 3; 2026: 3.

29 papers.

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  1. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  2. 2022Pretrained Language Models for Text Generation: A Surveycited by 121
  3. 2023Using Large Language Models for Zero-Shot Natural Language Generation from Knowledge Graphscited by 24
  4. 2022Learning to Transfer Prompts for Text Generationcited by 22
  5. 2024Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Miningcited by 20
  6. 2023Evaluating Generative Models for Graph-to-Text Generationcited by 16
  7. 2022ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Modelscited by 14
  8. 2024Research Trends for the Interplay between Large Language Models and Knowledge Graphscited by 14
  9. 2024Efficient Knowledge Infusion via KG-LLM Alignmentcited by 13
  10. 2024Graph Language Modelscited by 6
  11. 2025Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applicationscited by 6
  12. 2023Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generationcited by 5
17 more
  1. 2022ElitePLM: An Empirical Study on General Language Ability Evaluation of Pretrained Language Modelscited by 4
  2. 2024Narrating Causal Graphs with Large Language Modelscited by 4
  3. 2021Document Automation Architectures and Technologies: A Surveycited by 3
  4. 2023Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learningcited by 2
  5. 2023AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasetscited by 2
  6. 2023Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learningcited by 2
  7. 2023Biomedical Entity Linking with Triple-aware Pre-Trainingcited by 2
  8. 2024GraSAME: Injecting Token-Level Structural Information to Pretrained Language Models via Graph-guided Self-Attention Mechanismcited by 2
  9. 2022Syntax Controlled Knowledge Graph-to-Text Generation with Order and Semantic Consistencycited by 1
  10. 2023Document Automation Architectures: Updated Survey in Light of Large Language Modelscited by 1
  11. 2024GNNavi: Navigating the Information Flow in Large Language Models by Graph Neural Networkcited by 1
  12. 2024Unsupervised Learning of Graph from Recipescited by 0
  13. 2025Evaluating and Improving Graph to Text Generation with Large Language Modelscited by 0
  14. 2025Deep Representation Learning for Electronic Design Automationcited by 0
  15. 2026Competency Questions as Executable Plans: a Controlled RAG Architecture for Cultural Heritage Storytellingcited by 0
  16. 2026Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Surveycited by 0
  17. 2026GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generationcited by 0