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Investigating the reasoning abilities of transformer models, and discovering new challenging tasks for them, has been a topic of much interest.
RoBERTa: A robustly optimized bert pretraining approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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Huggingface’s transformers: State-of-the-art natural language processing
Wolf, T.; Debut, L.; Sanh, V.; Chaumond, J.; Delangue, C.; Moi, A.; Cistac, P.; Rault, T.; Louf, R.; Funtowicz, M.; et al. 2019 · 1910
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The complexity of theorem-proving procedures
Cook, S. A. 1971 · 1971
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Some results on quantifiers
Westerståhl, D.; et al. 1984 · 1984
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Where the really hard problems are
Cheeseman, P. C.; Kanefsky, B.; Taylor, W. M.; et al. 1991 · 1991
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Planning as Satisfiability
Kautz, H. A.; Selman, B.; et al. 1992 · 1992
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Computability, complexity, and languages: fundamentals of theoretical computer science
Davis, M.; Sigal, R.; and Weyuker, E. J. 1994 · 1994
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Encoding plans in propositional logic
Kautz, H.; McAllester, D.; and Selman, B. 1996 · 1996
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Some pitfalls for experimenters with random SAT
Mitchell, D. G.; and Levesque, H. J. 1996 · 1996
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Generating hard satisfiability problems
Selman, B.; Mitchell, D. G.; and Levesque, H. J. 1996 · 1996
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An Efficient Algorithm for Unit Propagation
Zhang, H.; and Stickel, M. E. 1996 · 1996
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Finding hard instances of the satisfiability problem: A survey
Cook, S. A.; and Mitchell, D. G. 1997 · 1997
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Determining computational complexity from characteristic ‘phase transitions’
Monasson, R.; Zecchina, R.; Kirkpatrick, S.; Selman, B.; and Troyansky, L. 1999 · 1999
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Computing Science: On the Threshold
Hayes, B. 2003 · 2003
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Longformer: The long-document transformer
Beltagy, I.; Peters, M. E.; and Cohan, A. 2020 · 2004
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Fragments of language
Pratt-Hartmann, I. 2004 · 2004
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More fragments of language
Pratt-Hartmann, I.; Third, A.; et al. 2006 · 2006
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Z3: An efficient SMT solver
De Moura, L.; and Bjørner, N. 2008 · 2008
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Logics for the relational syllogistic
Pratt-Hartmann, I.; and Moss, L. S. 2009 · 2009
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The data complexity of the syllogistic fragments of English
Thorne, C.; and Calvanese, D. 2010 · 2010
Cited alongside, same era.
The international SAT solver competitions
Järvisalo, M.; Le Berre, D.; Roussel, O.; and Simon, L. 2012 · 2012
Cited alongside, same era.
Neural programmer-interpreters
Reed, S.; and De Freitas, N. 2015 · 2015
Cited alongside, same era.
Pointer networks
Vinyals, O.; Fortunato, M.; and Jaitly, N. 2015 · 2015
Cited alongside, same era.
Towards AI-complete question answering: A set of prerequisite toy tasks
Weston, J.; Bordes, A.; Chopra, S.; Rush, A. M.; van Merriënboer, B.; Joulin, A.; and Mikolov, T. 2015 · 2015
Cited alongside, same era.
Learning to compose neural networks for question answering
Compositionality decomposed: how do neural networks generalise?
Hupkes, D.; Dankers, V.; Mul, M.; and Bruni, E. 2020 · 2020
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Are Pre-trained Language Models as Symbolic Reasoners over Knowledge?
Kassner, N.; Kroje, B.; and Schütze, H. 2020 · 2020
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Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 2020
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Beyond accuracy: Behavioral testing of NLP models with CheckList
Ribeiro, M. T.; Wu, T.; Guestrin, C.; and Singh, S. 2020 · 2020
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Probing natural language inference models through semantic fragments
Richardson, K.; Hu, H.; Moss, L.; and Sabharwal, A. 2020 · 2020
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PRover: Proof generation for interpretable reasoning over rules
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Andreas, J.; Rohrbach, M.; Darrell, T.; and Klein, D. 2016 · 2016
Cited alongside, same era.
Assessing the ability of LSTMs to learn syntax-sensitive dependencies
Linzen, T.; Dupoux, E.; and Goldberg, Y. 2016 · 2016
Cited alongside, same era.
Quantifiers and cognition: Logical and computational perspectives , volume 96
Szymanik, J.; et al. 2016 · 2016
Cited alongside, same era.
Making neural programming architectures generalize via recursion
Cai, J.; Shin, R.; and Song, D. 2017 · 2017
Cited alongside, same era.
Can Neural Networks Understand Logical Entailment?
Evans, R.; Saxton, D.; Amos, D.; Kohli, P.; and Grefenstette, E. 2018 · 2018
Cited alongside, same era.
AllenNLP: A deep semantic natural language processing platform
Gardner, M.; Grus, J.; Neumann, M.; Tafjord, O.; Dasigi, P.; Liu, N.; Peters, M.; Schmitz, M.; and Zettlemoyer, L. 2018 · 2018
Cited alongside, same era.
Annotation artifacts in natural language inference data
Gururangan, S.; Swayamdipta, S.; Levy, O.; Schwartz, R.; Bowman, S. R.; and Smith, N. A. 2018 · 2018
Cited alongside, same era.
Saha, S.; Ghosh, S.; Srivastava, S.; and Bansal, M. 2020 · 2020
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oLMpics–On what Language Model Pre-training Captures
Talmor, A.; Elazar, Y.; Goldberg, Y.; and Berant, J. 2020 · 2020
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CLUE: A Chinese Language Understanding Evaluation Benchmark
Xu, L.; Hu, H.; Zhang, X.; Li, L.; Cao, C.; Li, Y.; Xu, Y.; Sun, K.; Yu, D.; Yu, C.; Tian, Y.; Dong, Q.; Liu, W.; Shi, B.; Cui, Y.; Li, J.; Zeng, J.; Wang, R.; Xie, W.; Li, Y.; Patterson, Y.; Tian, Z.; Zhang, Y.; Zhou, H.; Liu, S.; Zhao, Z.; Zhao, Q.; Yue, C.; Zhang, X.; Yang, Z.; Richardson, K.; and Lan, Z. 2020 · 2020
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Do Neural Models Learn Systematicity of Monotonicity Inference in Natural Language?
Yanaka, H.; Mineshima, K.; Bekki, D.; and Inui, K. 2020 · 2020
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Thinking Aloud: Dynamic Context Generation Improves Zero-Shot Reasoning Performance of GPT-2
Betz, G.; Richardson, K.; and Voigt, C. 2021 · 2021
Closest in time.
Critical Thinking for Language Models
Betz, G.; Voigt, C.; and Richardson, K. 2021 · 2021
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Sensitivity as a Complexity Measure for Sequence Classification Tasks
Hahn, M.; Jurafsky, D.; and Futrell, R. 2021 · 2021
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Text modular networks: Learning to decompose tasks in the language of existing models
Khot, T.; Khashabi, D.; Richardson, K.; Clark, P.; and Sabharwal, A. 2021 · 2021
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Explainable Multi-hop Verbal Reasoning Through Internal Monologue
Liang, Z.; Bethard, S.; and Surdeanu, M. 2021 · 2021
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A Generative Symbolic Model for More General Natural Language Understanding and Reasoning
Saparov, A.; and Mitchell, T. M. 2021 · 2021
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Proofwriter: Generating implications, proofs, and abductive statements over natural language
Tafjord, O.; Mishra, B. D.; and Clark, P. 2021 · 2021
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Dyna-bAbI: unlocking bAbI’s potential with dynamic synthetic benchmarking
Tamari, R.; Richardson, K.; Sar-Shalom, A.; Kahlon, N.; Liu, N.; Tsarfaty, R.; and Shahaf, D. 2021 · 2021
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AND does not mean OR: Using Formal Languages to Study Language Models’ Representations
Traylor, A.; Feiman, R.; and Pavlick, E. 2021 · 2021
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ReaSCAN: Compositional Reasoning in Language Grounding
Wu, Z.; Kreiss, E.; Ong, D. C.; and Potts, C. 2021 · 2021
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