mlsanityData to 7 Oct 2026

Large Language Models Meet Knowledge Graphs to Answer Factoid Questions

arXiv 2310.02166 ↗First posted 3 Oct 2023cs.CLcited by 11

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Abstract (arXiv)

Recently, it has been shown that the incorporation of structured knowledge into Large Language Models significantly improves the results for a variety of NLP tasks. In this paper, we propose a method for exploring pre-trained Text-to-Text Language Models enriched with additional information from Knowledge Graphs for answering factoid questions. More specifically, we propose an algorithm for subgraphs extraction from a Knowledge Graph based on question entities and answer candidates. Then, we procure easily interpreted information with Transformer-based models through the linearization of the extracted subgraphs. Final re-ranking of the answer candidates with the extracted information boosts Hits@1 scores of the pre-trained text-to-text language models by 4-6%.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 12 of its 31 references that are in the map. First posted on arXiv 3 Oct 2023.

Builds on

12 of 31 references.

References · 12

  1. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  2. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 973
  3. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 713
  4. 2016Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Modelscited by 299
  5. 2016Key-Value Memory Networks for Directly Reading Documentscited by 223
  6. 2019PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Textcited by 182
  7. 2022GreaseLM: Graph REASoning Enhanced Language Models for Question Answeringcited by 170
  8. 2021A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutionscited by 77
  9. 2022Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answeringcited by 71
  10. 2021Multilingual Autoregressive Entity Linkingcited by 50
  11. 2019KBQA: Learning Question Answering over QA Corpora and Knowledge Basescited by 44
  12. 2021End-to-End Entity Resolution and Question Answering Using Differentiable Knowledge Graphscited by 11

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 6; 2026: 1.

11 papers.

  1. 2024Generate-on-Graph: Treat LLM as both Agent and KG in Incomplete Knowledge Graph Question Answeringcited by 59
  2. 2024Mitigating Hallucinations in Large Language Models via Self-Refinement-Enhanced Knowledge Retrievalcited by 16
  3. 2024Entity Retrieval for Answering Entity-Centric Questionscited by 10
  4. 2025Personalized Graph-Based Retrieval for Large Language Modelscited by 10
  5. 2025LLM-Independent Adaptive RAG: Let the Question Speak for Itselfcited by 7
  6. 2025Large Language Models for Knowledge Graph Embedding: A Surveycited by 3
  7. 2024Pyramid-Driven Alignment: Pyramid Principle Guided Integration of Large Language Models and Knowledge Graphscited by 2
  8. 2025An Entity Linking Agent for Question Answeringcited by 2
  9. 2025BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignmentcited by 2
  10. 2025Improving LLM's Attachment to External Knowledge In Dialogue Generation Tasks Through Entity Anonymizationcited by 0
  11. 2026MHGraphBench: Knowledge Graph-Grounded Benchmarking of Mental Health Knowledge in Large Language Modelscited by 0