Fetching the paper…
Reading the bibliography…
Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc.
Advancing NLP with cognitive language processing signals
Nora Hollenstein, Maria Barrett, Marius Troendle, Francesco Bigiolli, Nicolas Langer, and Ce Zhang. 2019 · 1904
Earlier work this paper cites.
Shane Storks, Qiaozi Gao, and Joyce Y. Chai. 2019 · 1904
Earlier work this paper cites.
A learning algorithm for boltzmann machines
David H. Ackley, Geoffrey E. Hinton, and Terrence J. Sejnowski. 1985 · 1985
Earlier work this paper cites.
The logic of inheritance in frame systems
Gerhard Brewka. 1987 · 1987
Earlier work this paper cites.
Readings in nonmonotonic reasoning
Matthew L Ginsberg. 1987 · 1987
Earlier work this paper cites.
Analogical representations of naive physics
Francesco Gardin and Bernard Meltzer. 1989 · 1989
Earlier work this paper cites.
Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. 2020 · 2003
Earlier work this paper cites.
Formalizations of commonsense psychology
Andrew S. Gordon and Jerry R. Hobbs. 2004 · 2004
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa. 2012 · 2012
Earlier work this paper cites.
Abstract meaning representation for sembanking
Laura Banarescu, Claire Bonial, Shu Cai, Madalina Georgescu, Kira Griffitt, Ulf Hermjakob, Kevin Knight, Philipp Koehn, Martha Palmer, and Nathan Schneider. 2013 · 2013
Earlier work this paper cites.
Learning to solve arithmetic word problems with verb categorization
Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, and Nate Kushman. 2014 · 2014
Earlier work this paper cites.
Learning to automatically solve algebra word problems
Nate Kushman, Luke Zettlemoyer, Regina Barzilay, and Yoav Artzi. 2014 · 2014
Earlier work this paper cites.
Parsing Algebraic Word Problems into Equations
Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas Ang. 2015 · 2015
Earlier work this paper cites.
Solving general arithmetic word problems
Subhro Roy and Dan Roth. 2015 · 2015
Earlier work this paper cites.
Reasoning about Quantities in Natural Language
Subhro Roy, Tim Vieira, and Dan Roth. 2015 · 2015
Earlier work this paper cites.
How well do computers solve math word problems? large-scale dataset construction and evaluation
Danqing Huang, Shuming Shi, Chin-Yew Lin, Jian Yin, and Wei-Ying Ma. 2016 · 2016
Earlier work this paper cites.
MAWPS: A math word problem repository
Rik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman, and Hannaneh Hajishirzi. 2016 · 2016
Earlier work this paper cites.
Brenden M. Lake and Marco Baroni. 2017 · 2017
Earlier work this paper cites.
Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017a · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Earlier work this paper cites.
Deep neural solver for math word problems
Yan Wang, Xiaojiang Liu, and Shuming Shi. 2017 · 2017
Earlier work this paper cites.
Think you have solved question answering? try arc, the AI2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Earlier work this paper cites.
Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann N. Dauphin. 2018 · 2018
Earlier work this paper cites.
Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
Earlier work this paper cites.
Object hallucination in image captioning
Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, Trevor Darrell, and Kate Saenko. 2018 · 2018
Earlier work this paper cites.
MathQA: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019 · 2019
Earlier work this paper cites.
Commonsense knowledge mining from pretrained models
Joe Davison, Joshua Feldman, and Alexander M. Rush. 2019 · 2019
Earlier work this paper cites.
DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
Earlier work this paper cites.
Language models as knowledge bases?
Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, Patrick S. H. Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander H. Miller. 2019 · 2019
Earlier work this paper cites.
CLUTRR: A diagnostic benchmark for inductive reasoning from text
Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William L. Hamilton. 2019 · 2019
Earlier work this paper cites.
Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
Earlier work this paper cites.
From recognition to cognition: Visual commonsense reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
Earlier work this paper cites.
Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le bras, Jianfeng Gao, and Yejin Choi. 2020 · 2020
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Earlier work this paper cites.
Transformers as soft reasoners over language
Peter Clark, Oyvind Tafjord, and Kyle Richardson. 2020 · 2020
Earlier work this paper cites.
Machine reasoning: Technology, dilemma and future
Nan Duan, Duyu Tang, and Ming Zhou. 2020 · 2020
Earlier work this paper cites.
How can we know what language models know
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Earlier work this paper cites.
What is more likely to happen next? video-and-language future event prediction
Jie Lei, Licheng Yu, Tamara Berg, and Mohit Bansal. 2020 · 2020
Earlier work this paper cites.
A diverse corpus for evaluating and developing English math word problem solvers
Shen-yun Miao, Chao-Chun Liang, and Keh-Yih Su. 2020 · 2020
Earlier work this paper cites.
Visualcomet: Reasoning about the dynamic context of a still image
Jae Sung Park, Chandra Bhagavatula, Roozbeh Mottaghi, Ali Farhadi, and Yejin Choi. 2020 · 2020
Earlier work this paper cites.
Editing factual knowledge in language models
Nicola De Cao, Wilker Aziz, and Ivan Titov. 2021 · 2021
Earlier work this paper cites.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harrison Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Joshua Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. 2021 · 2021
Earlier work this paper cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021
Earlier work this paper cites.
Explaining answers with entailment trees
Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, and Peter Clark. 2021 · 2021
Earlier work this paper cites.
Excar: Event graph knowledge enhanced explainable causal reasoning
Li Du, Xiao Ding, Kai Xiong, Ting Liu, and Bing Qin. 2021 · 2021
Earlier work this paper cites.
Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021 · 2021
Earlier work this paper cites.
Pre-trained models: Past, present and future
Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Yuan Yao, Ao Zhang, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, and Jun Zhu. 2021 · 2021
Earlier work this paper cites.
Measuring mathematical problem solving with the MATH dataset
Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Earlier work this paper cites.
Investigating the limitations of the transformers with simple arithmetic tasks
Rodrigo Nogueira, Zhiying Jiang, and Jimmy Lin. 2021 · 2021
Earlier work this paper cites.
Siru Ouyang, Zhuosheng Zhang, and Hai Zhao. 2021 · 2021
Earlier work this paper cites.
Prompting contrastive explanations for commonsense reasoning tasks
Bhargavi Paranjape, Julian Michael, Marjan Ghazvininejad, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2021 · 2021
Earlier work this paper cites.
Cross-domain reasoning via template filling
Dheeraj Rajagopal, Vivek Khetan, Bogdan Sacaleanu, Anatole Gershman, Andrew E. Fano, and Eduard H. Hovy. 2021 · 2021
Earlier work this paper cites.
ProofWriter: Generating implications, proofs, and abductive statements over natural language
Oyvind Tafjord, Bhavana Dalvi, and Peter Clark. 2021 · 2021
Earlier work this paper cites.
From LSAT: the progress and challenges of complex reasoning
Siyuan Wang, Zhongkun Liu, Wanjun Zhong, Ming Zhou, Zhongyu Wei, Zhumin Chen, and Nan Duan. 2021 · 2021
Earlier work this paper cites.
Why do pretrained language models help in downstream tasks? an analysis of head and prompt tuning
Colin Wei, Sang Michael Xie, and Tengyu Ma. 2021 · 2021
Earlier work this paper cites.
A survey on green deep learning
Jingjing Xu, Wangchunshu Zhou, Zhiyi Fu, Hao Zhou, and Lei Li. 2021 · 2021
Earlier work this paper cites.
What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou. 2022 · 2022
Earlier work this paper cites.
Penguins don’t fly: Reasoning about generics through instantiations and exceptions
Emily Allaway, Jena D. Hwang, Chandra Bhagavatula, Kathleen R. McKeown, Doug Downey, and Yejin Choi. 2022 · 2022
Cited alongside, same era.
Exploring length generalization in large language models
Cem Anil, Yuhuai Wu, Anders Andreassen, Aitor Lewkowycz, Vedant Misra, Vinay V. Ramasesh, Ambrose Slone, Guy Gur-Ari, Ethan Dyer, and Behnam Neyshabur. 2022 · 2022
Cited alongside, same era.
Multi-step deductive reasoning over natural language: An empirical study on out-of-distribution generalisation
Qiming Bao, Alex Yuxuan Peng, Tim Hartill, Neset Tan, Zhenyun Deng, Michael Witbrock, and Jiamou Liu. 2022 · 2022
Cited alongside, same era.
Prompting is programming: A query language for large language models
Luca Beurer-Kellner, Marc Fischer, and Martin Vechev. 2022 · 2022
Cited alongside, same era.
Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Kathleen S. Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed H. Chi, and Quoc Le. 2022 · 2022
Closest in time.
Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. 2022 · 2022
Closest in time.
Emergent analogical reasoning in large language models
Taylor W. Webb, Keith J. Holyoak, and Hongjing Lu. 2022 · 2022
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Commonsense knowledge reasoning and generation with pre-trained language models: A survey
Prajjwal Bhargava and Vincent Ng. 2022 · 2022
Cited alongside, same era.
E-KAR: A benchmark for rationalizing natural language analogical reasoning
Jiangjie Chen, Rui Xu, Ziquan Fu, Wei Shi, Zhongqiao Li, Xinbo Zhang, Changzhi Sun, Lei Li, Yanghua Xiao, and Hao Zhou. 2022a · 2022
Cited alongside, same era.
Large language models are few(1)-shot table reasoners
Wenhu Chen. 2022 · 2022
Cited alongside, same era.
Binding language models in symbolic languages
Zhoujun Cheng, Tianbao Xie, Peng Shi, Chengzu Li, Rahul Nadkarni, Yushi Hu, Caiming Xiong, Dragomir Radev, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu. 2022 · 2022
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, Parker Schuh, Kensen Shi, Sasha Tsvyashchenko, Joshua Maynez, Abhishek Rao, Parker Barnes, Yi Tay, Noam Shazeer, Vinodkumar Prabhakaran, Emily Reif, Nan Du, Ben Hutchinson, Reiner Pope, James Bradbury, Jacob Austin, Michael Isard, Guy Gur-Ari, Pengcheng Yin, Toju Duke, Anselm Levskaya, Sanjay Ghemawat, Sunipa Dev, Henryk Michalewski, Xavier Garcia, Vedant Misra, Kevin Robinson, Liam Fedus, Denny Zhou, Daphne Ippolito, David Luan, Hyeontaek Lim, Barret Zoph, Alexander Spiridonov, Ryan Sepassi, David Dohan, Shivani Agrawal, Mark Omernick, Andrew M. Dai, Thanumalayan Sankaranarayana Pillai, Marie Pellat, Aitor Lewkowycz, Erica Moreira, Rewon Child, Oleksandr Polozov, Katherine Lee, Zongwei Zhou, Xuezhi Wang, Brennan Saeta, Mark Diaz, Orhan Firat, Michele Catasta, Jason Wei, Kathy Meier-Hellstern, Douglas Eck, Jeff Dean, Slav Petrov, and Noah Fiedel. 2022 · 2022
Cited alongside, same era.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Y. Zhao, Yanping Huang, Andrew M. Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
Cited alongside, same era.
Faithful reasoning using large language models
Antonia Creswell and Murray Shanahan. 2022 · 2022
Cited alongside, same era.
Selection-inference: Exploiting large language models for interpretable logical reasoning
Antonia Creswell, Murray Shanahan, and Irina Higgins. 2022 · 2022
Cited alongside, same era.
Albert Webson and Ellie Pavlick. 2022 · 2022
Closest in time.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, Quoc Le, and Denny Zhou. 2022b · 2022
Closest in time.
Large language models are reasoners with self-verification
Yixuan Weng, Minjun Zhu, Shizhu He, Kang Liu, and Jun Zhao. 2022 · 2022
Closest in time.
Reframing human-ai collaboration for generating free-text explanations
Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark O. Riedl, and Yejin Choi. 2022 · 2022
Closest in time.
Promptchainer: Chaining large language model prompts through visual programming
Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, and Carrie J. Cai. 2022a · 2022
Closest in time.
An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma. 2022 · 2022
Closest in time.
A systematic evaluation of large language models of code
Frank F. Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn. 2022 · 2022
Closest in time.
SEQZERO: few-shot compositional semantic parsing with sequential prompts and zero-shot models
Jingfeng Yang, Haoming Jiang, Qingyu Yin, Danqing Zhang, Bing Yin, and Diyi Yang. 2022a · 2022
Closest in time.
LogicSolver: Towards interpretable math word problem solving with logical prompt-enhanced learning
Zhicheng Yang, Jinghui Qin, Jiaqi Chen, Liang Lin, and Xiaodan Liang. 2022b · 2022
Closest in time.
How does gpt obtain its ability? tracing emergent abilities of language models to their sources
Fu Yao, Peng Hao, and Khot Tushar. 2022 · 2022
Closest in time.
The unreliability of explanations in few-shot prompting for textual reasoning
Xi Ye and Greg Durrett. 2022 · 2022
Closest in time.
Abductionrules: Training transformers to explain unexpected inputs
Nathan Young, Qiming Bao, Joshua Bensemann, and Michael Witbrock. 2022 · 2022
Closest in time.
ALERT: adapting language models to reasoning tasks
Ping Yu, Tianlu Wang, Olga Golovneva, Badr AlKhamissy, Gargi Ghosh, Mona T. Diab, and Asli Celikyilmaz. 2022 · 2022
Closest in time.
Weizhe Yuan and Pengfei Liu. 2022 · 2022
Closest in time.
STar: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. 2022 · 2022
Closest in time.
The impact of symbolic representations on in-context learning for few-shot reasoning
Hanlin Zhang, YiFan Zhang, Li Erran Li, and Eric Xing. 2022 · 2022
Closest in time.
What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou. 2023 · 2023
Closest in time.
Contrastive learning with logic-driven data augmentation for logical reasoning over text
Qiming Bao, Alex Yuxuan Peng, Zhenyun Deng, Wanjun Zhong, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Michael Witbrock, and Jiamou Liu. 2023 · 2023
Closest in time.
See, think, confirm: Interactive prompting between vision and language models for knowledge-based visual reasoning
Zhenfang Chen, Qinhong Zhou, Yikang Shen, Yining Hong, Hao Zhang, and Chuang Gan. 2023 · 2023
Closest in time.
Editing language model-based knowledge graph embeddings
Siyuan Cheng, Ningyu Zhang, Bozhong Tian, Zelin Dai, Feiyu Xiong, Wei Guo, and Huajun Chen. 2023 · 2023
Closest in time.
Active prompting with chain-of-thought for large language models
Shizhe Diao, Pengcheng Wang, Yong Lin, and Tong Zhang. 2023 · 2023
Closest in time.
A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, and Zhifang Sui. 2023 · 2023
Closest in time.
Rethinking with retrieval: Faithful large language model inference
Hangfeng He, Hongming Zhang, and Dan Roth. 2023 · 2023
Closest in time.
Language is not all you need: Aligning perception with language models
Shaohan Huang, Li Dong, Wenhui Wang, Yaru Hao, Saksham Singhal, Shuming Ma, Tengchao Lv, Lei Cui, Owais Khan Mohammed, Barun Patra, Qiang Liu, Kriti Aggarwal, Zewen Chi, Johan Bjorck, Vishrav Chaudhary, Subhojit Som, Xia Song, and Furu Wei. 2023 · 2023
Closest in time.
Mathprompter: Mathematical reasoning using large language models
Shima Imani, Liang Du, and Harsh Shrivastava. 2023 · 2023
Closest in time.
Decomposed prompting: A modular approach for solving complex tasks
Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal. 2023 · 2023
Closest in time.
Mind’s eye: Grounded language model reasoning through simulation
Ruibo Liu, Jason Wei, Shixiang Shane Gu, Te-Yen Wu, Soroush Vosoughi, Claire Cui, Denny Zhou, and Andrew M. Dai. 2023 · 2023
Closest in time.
Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch. 2023 · 2023
Closest in time.
Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Sean Welleck, Bodhisattwa Prasad Majumder, Shashank Gupta, Amir Yazdanbakhsh, and Peter Clark. 2023 · 2023
Closest in time.
Augmented language models: a survey
Grégoire Mialon, Roberto Dessì, Maria Lomeli, Christoforos Nalmpantis, Ramakanth Pasunuru, Roberta Raileanu, Baptiste Rozière, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, and Thomas Scialom. 2023 · 2023
Closest in time.
Boosting theory-of-mind performance in large language models via prompting
Shima Rahimi Moghaddam and Christopher J. Honey. 2023 · 2023
Closest in time.
OpenAI. 2023 · 2023
Closest in time.
Easyinstruct: An easy-to-use framework to instruct large language models
Yixin Ou, Shengyu Mao, Lei Li, Ziwen Xu, Xiaolong Weng, Shuofei Qiao, Yuqi Zhu, Yinuo Jiang, Zhen Bi, Jing Chen, Huajun Chen, and Ningyu Zhang. 2023 · 2023
Closest in time.
ART: automatic multi-step reasoning and tool-use for large language models
Bhargavi Paranjape, Scott M. Lundberg, Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, and Marco Túlio Ribeiro. 2023 · 2023
Closest in time.
REFINER: reasoning feedback on intermediate representations
Debjit Paul, Mete Ismayilzada, Maxime Peyrard, Beatriz Borges, Antoine Bosselut, Robert West, and Boi Faltings. 2023 · 2023
Closest in time.
Why think step-by-step? reasoning emerges from the locality of experience
Ben Prystawski and Noah D. Goodman. 2023 · 2023
Closest in time.
Tool learning with foundation models
Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, Yi Ren Fung, Yusheng Su, Huadong Wang, Cheng Qian, Runchu Tian, Kunlun Zhu, Shihao Liang, Xingyu Shen, Bokai Xu, Zhen Zhang, Yining Ye, Bowen Li, Ziwei Tang, Jing Yi, Yuzhang Zhu, Zhenning Dai, Lan Yan, Xin Cong, Yaxi Lu, Weilin Zhao, Yuxiang Huang, Junxi Yan, Xu Han, Xian Sun, Dahai Li, Jason Phang, Cheng Yang, Tongshuang Wu, Heng Ji, Zhiyuan Liu, and Maosong Sun. 2023 · 2023
Closest in time.
Iterated decomposition: Improving science q&a by supervising reasoning processes
Justin Reppert, Ben Rachbach, Charlie George, Luke Stebbing, JungWon Byun, Maggie Appleton, and Andreas Stuhlmüller. 2023 · 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 · 2023
Closest in time.
Synthetic prompting: Generating chain-of-thought demonstrations for large language models
Zhihong Shao, Yeyun Gong, Yelong Shen, Minlie Huang, Nan Duan, and Weizhu Chen. 2023 · 2023
Closest in time.
Clever hans or neural theory of mind? stress testing social reasoning in large language models
Natalie Shapira, Mosh Levy, Seyed Hossein Alavi, Xuhui Zhou, Yejin Choi, Yoav Goldberg, Maarten Sap, and Vered Shwartz. 2023 · 2023
Closest in time.
Reflexion: an autonomous agent with dynamic memory and self-reflection
Noah Shinn, Beck Labash, and Ashwin Gopinath. 2023 · 2023
Closest in time.
Automatic prompt augmentation and selection with chain-of-thought from labeled data
Kashun Shum, Shizhe Diao, and Tong Zhang. 2023 · 2023
Closest in time.
Selective annotation makes language models better few-shot learners
Hongjin SU, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu. 2023 · 2023
Closest in time.
Vipergpt: Visual inference via python execution for reasoning
Dídac Surís, Sachit Menon, and Carl Vondrick. 2023 · 2023
Closest in time.
PINTO: Faithful language reasoning using prompt-generated rationales
PeiFeng Wang, Aaron Chan, Filip Ilievski, Muhao Chen, and Xiang Ren. 2023 · 2023
Closest in time.
Visual chatgpt: Talking, drawing and editing with visual foundation models
Chenfei Wu, Shengming Yin, Weizhen Qi, Xiaodong Wang, Zecheng Tang, and Nan Duan. 2023 · 2023
Closest in time.
MM-REACT: prompting chatgpt for multimodal reasoning and action
Zhengyuan Yang, Linjie Li, Jianfeng Wang, Kevin Lin, Ehsan Azarnasab, Faisal Ahmed, Zicheng Liu, Ce Liu, Michael Zeng, and Lijuan Wang. 2023 · 2023
Closest in time.
Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, and Yongbin Li. 2023 · 2023
Closest in time.
Answering questions by meta-reasoning over multiple chains of thought
Ori Yoran, Tomer Wolfson, Ben Bogin, Uri Katz, Daniel Deutch, and Jonathan Berant. 2023 · 2023
Closest in time.
A survey of large language models
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen. 2023 · 2023
Closest in time.
Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi. 2023 · 2023
Closest in time.
Are NLP models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal. 2021 · 2094
Closest in time.