mlsanityData to 7 Oct 2026

Answer Candidate Type Selection: Text-to-Text Language Model for Closed Book Question Answering Meets Knowledge Graphs

arXiv 2310.07008 ↗First posted 10 Oct 2023cs.CL cs.AI cs.IRcited by 2

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Abstract (arXiv)

Pre-trained Text-to-Text Language Models (LMs), such as T5 or BART yield promising results in the Knowledge Graph Question Answering (KGQA) task. However, the capacity of the models is limited and the quality decreases for questions with less popular entities. In this paper, we present a novel approach which works on top of the pre-trained Text-to-Text QA system to address this issue. Our simple yet effective method performs filtering and re-ranking of generated candidates based on their types derived from Wikidata "instance_of" property.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 24 references that are in the map. First posted on arXiv 10 Oct 2023.

Builds on

13 of 24 references.

References · 13

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  4. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  5. 2020How Much Knowledge Can You Pack Into the Parameters of a Language Model?cited by 713
  6. 2016Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Modelscited by 299
  7. 2015Large-scale Simple Question Answering with Memory Networkscited by 127
  8. 2019PyTorch-BigGraph: A Large-scale Graph Embedding Systemcited by 118
  9. 2022Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answeringcited by 71
  10. 2021Multilingual Autoregressive Entity Linkingcited by 50
  11. 2018Towards a Question Answering System over the Semantic Webcited by 14
  12. 2020RuBQ: A Russian Dataset for Question Answering over Wikidatacited by 14
1 more
  1. 2022What do we Really Know about State of the Art NER?cited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunitiescited by 30
  2. 2026LLMs+Graphs: Toward Graph-Native, Synergistic AI Systemscited by 0