mlsanityData to 13 Oct 2026

Careful Selection of Knowledge to solve Open Book Question Answering

arXiv 1907.10738 ↗First posted 24 Jul 2019cs.CL cs.AI cs.IRcited by 35

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Abstract (arXiv)

Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic. Recently a challenge involving such QA, OpenBookQA, has been proposed. Unlike most other NLQA tasks that focus on linguistic understanding, OpenBookQA requires deeper reasoning involving linguistic understanding as well as reasoning with common knowledge. In this paper we address QA with respect to the OpenBookQA dataset and combine state of the art language models with abductive information retrieval (IR), information gain based re-ranking, passage selection and weighted scoring to achieve 72.0% accuracy, an 11.6% improvement over the current state of the art.

Key facts. Cited by 35 papers in the map (data through 13 Oct 2026). First posted on arXiv 24 Jul 2019.

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Citing papers per year: 2021: 12; 2022: 3; 2023: 3; 2024: 7; 2025: 6; 2026: 4.

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  1. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 386
  2. 2021Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answeringcited by 178
  3. 2023On the Impact of Calibration Data in Post-training Quantization and Pruningcited by 53
  4. 2021Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answeringcited by 50
  5. 2021JointLK: Joint Reasoning with Language Models and Knowledge Graphs for Commonsense Question Answeringcited by 40
  6. 2024Basis Sharing: Cross-Layer Parameter Sharing for Large Language Model Compressioncited by 35
  7. 2024SGLP: A Similarity Guided Fast Layer Partition Pruning for Compressing Large Deep Modelscited by 24
  8. 2024Wings: Learning Multimodal LLMs without Text-only Forgettingcited by 23
  9. 2021GNN is a Counter? Revisiting GNN for Question Answeringcited by 21
  10. 2021Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answeringcited by 17
  11. 2021Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matricescited by 17
  12. 2024Enhancing Ethical Explanations of Large Language Models through Iterative Symbolic Refinementcited by 16
23 more
  1. 2021Exploiting Reasoning Chains for Multi-hop Science Question Answeringcited by 15
  2. 2022"John is 50 years old, can his son be 65?" Evaluating NLP Models' Understanding of Feasibilitycited by 15
  3. 2024CCI3.0-HQ: a large-scale Chinese dataset of high quality designed for pre-training large language modelscited by 11
  4. 2024Self-calibration for Language Model Quantization and Pruningcited by 10
  5. 2022Elaboration-Generating Commonsense Question Answering at Scalecited by 9
  6. 2025Generalized Fisher-Weighted SVD: Scalable Kronecker-Factored Fisher Approximation for Compressing Large Language Modelscited by 8
  7. 2023Long-Tailed Question Answering in an Open Worldcited by 7
  8. 2025Layer-wise dynamic rank for compressing large language modelscited by 7
  9. 2021Zero-Shot Open-Book Question Answeringcited by 6
  10. 2025Compressing Language Models for Specialized Domainscited by 4
  11. 2021Encoding Explanatory Knowledge for Zero-shot Science Question Answeringcited by 3
  12. 2025HASSLE-free: A unified Framework for Sparse plus Low-Rank Matrix Decomposition for LLMscited by 3
  13. 2026ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Modelscited by 3
  14. 2023KEPR: Knowledge Enhancement and Plausibility Ranking for Generative Commonsense Question Answeringcited by 2
  15. 2026Correctness-Optimized Residual Activation Lens (CORAL): Transferrable and Calibration-Aware Inference-Time Steeringcited by 2
  16. 20263BASiL: An Algorithmic Framework for Sparse plus Low-Rank Compression of LLMscited by 2
  17. 2022Semantic Sentence Composition Reasoning for Multi-Hop Question Answeringcited by 1
  18. 2024Large Language Models aren't all that you needcited by 1
  19. 2025Rethinking Parameter Sharing as Graph Coloring for Structured Compressioncited by 1
  20. 2026∞-MoE: Generalizing Mixture of Experts to Infinite Expertscited by 1
  21. 2021Understanding in Artificial Intelligencecited by 0
  22. 2021ActKnow: Active External Knowledge Infusion Learning for Question Answering in Low Data Regimecited by 0
  23. 2025TSENOR: Highly-Efficient Algorithm for Finding Transposable N:M Sparse Maskscited by 0