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Combining deep learning with symbolic logic reasoning aims to capitalize on the success of both fields and is drawing increasing attention.
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Compositional attention networks for machine reasoning,
D. A. Hudson, C. D. Manning, · 2018
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Deeplogic: Towards end-to-end differentiable logical reasoning,
N. Cingillioglu, A. Russo, · 2019
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Roberta: A robustly optimized bert pretraining approach,
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, V. Stoyanov, · 2019
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Neural logic reinforcement learning,
Z. Jiang, S. Luo, · 2019
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Relating blindsight and ai: A review,
J. Bensemann, Q. Bao, G. Gendron, T. Hartill, M. Witbrock, · 2022
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Deepqr: Neural-based quality ratings for learnersourced multiple-choice questions,
L. Ni, Q. Bao, X. Li, Q. Qi, P. Denny, J. Warren, M. Witbrock, J. Liu, · 2022
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Multi2Claim: Generating scientific claims from multi-choice questions for scientific fact-checking,
N. Tan, T. Nguyen, J. Bensemann, A. Peng, Q. Bao, Y. Chen, M. Gahegan, M. Witbrock, · 2023
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Large language models are not strong abstract reasoners,
G. Gendron, Q. Bao, M. Witbrock, G. Dobbie, · 2023
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Input-length-shortening and text generation via attention values,
N. Ö. Tan, A. Y. Peng, J. Bensemann, Q. Bao, T. Hartill, M. Gahegan, M. Witbrock, · 2023
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J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, · 2019
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fairseq: A fast, extensible toolkit for sequence modeling,
M. Ott, S. Edunov, A. Baevski, A. Fan, S. Gross, N. Ng, D. Grangier, M. Auli, · 2019
Cited alongside, same era.
Transformers as soft reasoners over language,
P. Clark, O. Tafjord, K. Richardson, · 2020
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Probing natural language inference models through semantic fragments.,
K. Richardson, H. Hu, L. S. Moss, A. Sabharwal, · 2020
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Hhh: An online medical chatbot system based on knowledge graph and hierarchical bi-directional attention,
Q. Bao, L. Ni, J. Liu, · 2020
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From symbolic logic reasoning to soft reason-ing: A neural-symbolic paradigm,
Q. Bao, M. Witbrock, J. Liu, · 2021
Cited alongside, same era.
AbductionRules: Training transformers to explain unexpected inputs,
N. Young, Q. Bao, J. Bensemann, M. Witbrock, · 2022
Cited alongside, same era.
Q. Bao, G. Gendron, A. Y. Peng, W. Zhong, N. Tan, Y. Chen, M. Witbrock, J. Liu, · 2023
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Q. Qi, Q. Bao, A. Y. Peng, J. Liu, M. Witbrock, A dynamic prompt-tuning method for data augmentation with associated knowledge, 2023. URL: https://openreview.net/forum?id=hli7A0ioiS_
2023
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Chatlogic: Integrating logic programming with large language models for multi-step reasoning,
Z. Wang, J. Liu, Q. Bao, H. Rong, J. Zhang, · 2024
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Cora: Optimizing low-rank adaptation with common subspace of large language models,
X. Xiao, S. Shen, Q. Bao, H. Rong, K. Liu, Z. Wang, J. Liu, · 2024
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Abstract Meaning Representation-based logic-driven data augmentation for logical reasoning,
Q. Bao, A. Peng, Z. Deng, W. Zhong, G. Gendron, T. Pistotti, N. Tan, N. Young, Y. Chen, Y. Zhu, P. Denny, M. Witbrock, J. Liu, · 2024
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Exploring iterative enhancement for improving learnersourced multiple-choice question explanations with large language models,
Q. Bao, J. Leinonen, A. Y. Peng, W. Zhong, G. Gendron, T. Pistotti, A. Huang, P. Denny, M. Witbrock, J. Liu, · 2025
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Q. Bao, Developing And Assessing Language Models For Logical Reasoning Over Natural Language, Ph.D. thesis, University of Auckland, 2025
2025
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