mlsanityData to 7 Oct 2026

Task-specific Pre-training and Prompt Decomposition for Knowledge Graph Population with Language Models

arXiv 2208.12539 ↗First posted 26 Aug 2022cs.CLcited by 6

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Abstract (arXiv)

We present a system for knowledge graph population with Language Models, evaluated on the Knowledge Base Construction from Pre-trained Language Models (LM-KBC) challenge at ISWC 2022. Our system involves task-specific pre-training to improve LM representation of the masked object tokens, prompt decomposition for progressive generation of candidate objects, among other methods for higher-quality retrieval. Our system is the winner of track 1 of the LM-KBC challenge, based on BERT LM; it achieves 55.0% F-1 score on the hidden test set of the challenge.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 4 of its 8 references that are in the map. First posted on arXiv 26 Aug 2022.

Builds on

4 of 8 references.

References · 4

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,548
  3. 2019How Can We Know What Language Models Know?cited by 953
  4. 2022Prompting as Probing: Using Language Models for Knowledge Base Constructioncited by 32

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 3.

6 papers.

  1. 2024DrAttack: Prompt Decomposition and Reconstruction Makes Powerful LLM Jailbreakerscited by 91
  2. 2023Large Language Models and Knowledge Graphs: Opportunities and Challengescited by 71
  3. 2023LLM2KB: Constructing Knowledge Bases using instruction tuned context aware Large Language Modelscited by 4
  4. 2024Textual Entailment for Effective Triple Validation in Object Predictioncited by 2
  5. 2024UniArk: Improving Generalisation and Consistency for Factual Knowledge Extraction through Debiasingcited by 2
  6. 2023Expanding the Vocabulary of BERT for Knowledge Base Constructioncited by 1