Fetching the paper…
Reading the bibliography…
Logical reasoning is fundamental for humans yet presents a substantial challenge in the domain of Artificial Intelligence.
Artificial intelligence, logic and formalizing common sense
John McCarthy · 1989
Earlier work this paper cites.
Interpretation as abduction
Jerry R Hobbs, Mark E Stickel, Douglas E Appelt, and Paul Martin · 1993
Earlier work this paper cites.
Approaches to abductive reasoning: an overview
Gabriele Paul · 1993
Earlier work this paper cites.
The birth of prolog
Alain Colmerauer and Philippe Roussel · 1996
Earlier work this paper cites.
Markov logic networks
Matthew Richardson and Pedro Domingos · 2006
Earlier work this paper cites.
Problog: A probabilistic prolog and its application in link discovery
Luc De Raedt, Angelika Kimmig, and Hannu Toivonen · 2007
Earlier work this paper cites.
What Is Induction and Why Study It? , pp. 1–24
Evan Heit · 2007
Earlier work this paper cites.
Weighted rules under the stable model semantics
Joohyung Lee and Yi Wang · 2016
Earlier work this paper cites.
Hinge-loss markov random fields and probabilistic soft logic
Stephen H. Bach, Matthias Broecheler, Bert Huang, and Lise Getoor · 2017
Earlier work this paper cites.
Inductive reasoning
Bruno Sauce and Louis D Matzel · 2017
Earlier work this paper cites.
Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, and et al · 2017
Earlier work this paper cites.
Strong equivalence for lpmln programs
Joohyung Lee and Man Luo · 2018
Earlier work this paper cites.
Abductive commonsense reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Wen-tau Yih, and Yejin Choi · 2019
Earlier work this paper cites.
Human reasoning: The psychology of deduction
Ruth MJ Byrne, Jonathan St BT Evans, and Stephen E Newstead · 2019
Earlier work this paper cites.
Cosmos QA: Machine reading comprehension with contextual commonsense reasoning
Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2019
Earlier work this paper cites.
Abduction-based explanations for machine learning models
Alexey Ignatiev, Nina Narodytska, and Joao Marques-Silva · 2019
Earlier work this paper cites.
Answer set programming
Vladimir Lifschitz · 2019
Earlier work this paper cites.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, and et al · 2019
Earlier work this paper cites.
Probing neural network comprehension of natural language arguments
Timothy Niven and Hung-Yu Kao · 2019
Earlier work this paper cites.
Clutrr: A diagnostic benchmark for inductive reasoning from text
Koustuv Sinha, Shagun Sodhani, Jin Dong, and et al · 2019
Earlier work this paper cites.
Wiqa: A dataset for “what if…” reasoning over procedural text
Niket Tandon, Bhavana Dalvi, Keisuke Sakaguchi, Peter Clark, and Antoine Bosselut · 2019
Earlier work this paper cites.
Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman · 2019
Earlier work this paper cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, and et al. Carbonell · 2019
Earlier work this paper cites.
Reclor: A reading comprehension dataset requiring logical reasoning
Weihao Yu, Zihang Jiang, Yanfei Dong, and Jiashi Feng · 2019
Earlier work this paper cites.
Can transformers reason about effects of actions?
Pratyay Banerjee, Chitta Baral, Man Luo, Arindam Mitra, Kuntal Pal, Tran C Son, and Neeraj Varshney · 2020
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Transformers as soft reasoners over language
Peter Clark, Oyvind Tafjord, and Kyle Richardson · 2020
Cited alongside, same era.
TaxiNLI: Taking a ride up the NLU hill
Pratik Joshi, Somak Aditya, Aalok Sathe, and Monojit Choudhury · 2020
Cited alongside, same era.
Logiqa: A challenge dataset for machine reading comprehension with logical reasoning
Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang · 2020
Cited alongside, same era.
Adversarial nli: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela · 2020
Cited alongside, same era.
Prover: Proof generation for interpretable reasoning over rules
Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, and Mohit Bansal · 2020
Cited alongside, same era.
Reclor: A reading comprehension dataset requiring logical reasoning
Weihao Yu, Zihang Jiang, Yanfei Dong, and Jiashi Feng · 2020
Large language models are reasoning teachers
Se-Young Yun Namgyu Ho, Laura Schmid · 2022
Later among the works it cites.
In-boxbart: Get instructions into biomedical multi-task learning
Mihir Parmar, Swaroop Mishra, Mirali Purohit, Man Luo, Murad Mohammad, and Chitta Baral · 2022
Later among the works it cites.
Apollo: A simple approach for adaptive pretraining of language models for logical reasoning
Soumya Sanyal, Yichong Xu, Shuohang Wang, Ziyi Yang, Reid Pryzant, Wenhao Yu, Chenguang Zhu, and Xiang Ren · 2022
Later among the works it cites.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al · 2022
Later among the works it cites.
Chain-of-thought prompting elicits reasoning in large language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
WinoLogic: A zero-shot logic-based diagnostic dataset for Winograd Schema Challenge
Weinan He, Canming Huang, Yongmei Liu, and Xiaodan Zhu · 2021
Cited alongside, same era.
Logiqa: a challenge dataset for machine reading comprehension with logical reasoning
Jian Liu, Leyang Cui, Hanmeng Liu, Dandan Huang, Yile Wang, and Yue Zhang · 2021
Cited alongside, same era.
Unicorn on rainbow: A universal commonsense reasoning model on a new multitask benchmark
Nicholas Lourie, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi · 2021
Cited alongside, same era.
SPARTQA: A textual question answering benchmark for spatial reasoning
Roshanak Mirzaee, Hossein Rajaby Faghihi, Qiang Ning, and Parisa Kordjamshidi · 2021
Cited alongside, same era.
Pushing the limits of rule reasoning in transformers through natural language satisfiability
Kyle Richardson and Ashish Sabharwal · 2021
Cited alongside, same era.
RuleBERT: Teaching soft rules to pre-trained lms
Mohammed Saeed, Naser Ahmadi, Preslav Nakov, and Paolo Papotti · 2021
Cited alongside, same era.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Later among the works it cites.
On the paradox of learning to reason from data
Honghua Zhang, Liunian Harold Li, Tao Meng, Kai-Wei Chang, and Guy Van den Broeck · 2022
Later among the works it cites.
Logicbench: Towards systematic evaluation of logical reasoning ability of large language models
Anonymous · 2023
Closest in time.
Self-consistent narrative prompts on abductive natural language inference
Chunkit Chan, Xin Liu, Tsz Ho Chan, Jiayang Cheng, Yangqiu Song, Ginny Wong, and Simon See · 2023
Closest in time.
Language models can be logical solvers
Jiazhan Feng, Ruochen Xu, Junheng Hao, Hiteshi Sharma, Yelong Shen, Dongyan Zhao, and Weizhu Chen · 2023
Closest in time.
Lin Guan, Karthik Valmeekam, Sarath Sreedharan, and Subbarao Kambhampati · 2023
Closest in time.
Inductive reasoning in humans and large language models
Simon Jerome Han, Keith J Ransom, Andrew Perfors, and Charles Kemp · 2023
Closest in time.
CRoW: Benchmarking commonsense reasoning in real-world tasks
Mete Ismayilzada, Debjit Paul, Syrielle Montariol, Mor Geva, and Antoine Bosselut · 2023
Closest in time.
Logicllm: Exploring self-supervised logic-enhanced training for large language models
Fangkai Jiao, Zhiyang Teng, Shafiq Joty, Bosheng Ding, Aixin Sun, Zhengyuan Liu, and Nancy F Chen · 2023
Closest in time.
Measuring faithfulness in chain-of-thought reasoning
Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner, Carson Denison, Danny Hernandez, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, Kamile Lukosiute, Karina Nguyen, Newton Cheng, Nicholas Joseph, Nicholas Schiefer, Oliver Rausch, Robin Larson, Sam McCandlish, Sandipan Kundu, Saurav Kadavath, Shannon Yang, Thomas Henighan, Timothy Maxwell, Timothy Telleen-Lawton, Tristan Hume, Zac Hatfield-Dodds, Jared Kaplan, Jan Brauner, Samuel R. Bowman, and Ethan Perez · 2023
Closest in time.
Logiqa 2.0—an improved dataset for logical reasoning in natural language understanding
Hanmeng Liu, Jian Liu, Leyang Cui, Zhiyang Teng, Nan Duan, Ming Zhou, and Yue Zhang · 2023
Closest in time.
Chameleon: Plug-and-play compositional reasoning with large language models
Pan Lu, Baolin Peng, Hao Cheng, Michel Galley, Kai-Wei Chang, Ying Nian Wu, Song-Chun Zhu, and Jianfeng Gao · 2023
Closest in time.
Linc: A neuro-symbolic approach for logical reasoning by combining language models with first-order logic provers
Theo X. Olausson*, Alex Gu*, Ben Lipkin*, Cedegao E. Zhang*, Armando Solar-Lezama, Joshua B. Tenenbaum, and Roger P. Levy · 2023
Closest in time.
Logic-lm: Empowering large language models with symbolic solvers for faithful logical reasoning
Liangming Pan, Alon Albalak, Xinyi Wang, and William Yang Wang · 2023
Closest in time.
Art: Automatic multi-step reasoning and tool-use for large language models
Bhargavi Paranjape, Scott Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro · 2023
Closest in time.
Testing the general deductive reasoning capacity of large language models using ood examples
Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, Nitish Joshi, Seyed Mehran Kazemi, Najoung Kim, and He He · 2023
Closest in time.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
Closest in time.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al · 2023
Closest in time.
Cognitive architectures for language agents
Theodore Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas L Griffiths · 2023
Closest in time.
Glore: Evaluating logical reasoning of large language models
Zhiyang Teng, Ruoxi Ning, Jian Liu, Qiji Zhou, Yue Zhang, et al · 2023
Closest in time.
Can nlp models correctly reason over contexts that break the common assumptions?
Neeraj Varshney, Mihir Parmar, Nisarg Patel, Divij Handa, Sayantan Sarkar, Man Luo, and Chitta Baral · 2023
Closest in time.
Zhaofeng Wu, Linlu Qiu, Alexis Ross, Ekin Akyürek, Boyuan Chen, Bailin Wang, Najoung Kim, Jacob Andreas, and Yoon Kim · 2023
Closest in time.