Fetching the paper…
Reading the bibliography…
When people answer questions about a specific situation, e.g., "I cheated on my mid-term exam last week.
Reasoning over paragraph effects in situations
Kevin Lin, Oyvind Tafjord, Peter Clark, and Matt Gardner. 2019 · 1908
Earlier work this paper cites.
"a framework for representing knowledge"
Marvin Minsky. 1974 · 1974
Earlier work this paper cites.
The role of affect in narratives
Michael G. Dyer. 1983 · 1983
Earlier work this paper cites.
Mental Models
Dedre Gentner and Albert L. Stevens. 1983 · 1983
Earlier work this paper cites.
Mental Models : Towards a Cognitive Science of Language
P. Johnson-Laird. 1983 · 1983
Earlier work this paper cites.
Daydreaming in humans and computers
Erik T Mueller, Michael G Dyer, et al. 1985 · 1985
Earlier work this paper cites.
Daydreaming in humans and machines: a computer model of the stream of thought
Erik T Mueller. 1990 · 1990
Earlier work this paper cites.
The construction of explanations
Ruth MJ Byrne. 1991 · 1991
Earlier work this paper cites.
Mental models and causal explanation: Judgements of probable cause and explanatory relevance
D. Hilton. 1996 · 1996
Earlier work this paper cites.
Moral stories: Situated reasoning about norms, intents, actions, and their consequences
Denis Emelin, Ronan Le Bras, Jena D Hwang, Maxwell Forbes, and Yejin Choi. 2020 · 2012
Earlier work this paper cites.
Generating natural questions about an image
N. Mostafazadeh, Ishan Misra, Jacob Devlin, Margaret Mitchell, Xiaodong He, and Lucy Vanderwende. 2016 · 2016
Earlier work this paper cites.
Allennlp: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke S. Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani. 2017 · 2017
Earlier work this paper cites.
Modeling naive psychology of characters in simple commonsense stories
Hannah Rashkin, Antoine Bosselut, Maarten Sap, Kevin Knight, and Yejin Choi. 2018 · 2018
Earlier work this paper cites.
Improving machine reading comprehension with general reading strategies
Kai Sun, Dian Yu, Dong Yu, and Claire Cardie. 2018 · 2018
Earlier work this paper cites.
CODAH: An adversarially-authored question answering dataset for common sense
Michael Chen, Mike D’Arcy, Alisa Liu, Jared Fernandez, and Doug Downey. 2019 · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
Cited alongside, same era.
Social IQa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Adversarial filters of dataset biases
Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
Measuring and improving consistency in pretrained language models
Yanai Elazar, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Eduard Hovy, Hinrich Schütze, and Yoav Goldberg. 2021 · 2021
Closest in time.
Aligning ai with shared human values
Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Closest in time.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Closest in time.
What makes good in-context examples for GPT-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021 · 2021
Closest in time.
Think about it! improving defeasible reasoning by first modeling the question scenario
Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Peter Clark, Yiming Yang, and Eduard H. Hovy. 2021 · 2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Social chemistry 101: Learning to reason about social and moral norms
Maxwell Forbes, Jena D Hwang, Vered Shwartz, Maarten Sap, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
COMET-ATOMIC 2020: On symbolic and neural commonsense knowledge graphs
Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, Jeff Da, Keisuke Sakaguchi, Antoine Bosselut, and Yejin Choi. 2021 · 2020
Cited alongside, same era.
How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, J. Araki, and Graham Neubig. 2020 · 2020
Cited alongside, same era.
Bert-knn: Adding a knn search component to pretrained language models for better qa
Nora Kassner and Hinrich Schütze. 2020 · 2020
Cited alongside, same era.
Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis. 2020 · 2020
Cited alongside, same era.
Unifiedqa: Crossing format boundaries with a single QA system
Daniel Khashabi, Sewon Min, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, P. Clark, and Hannaneh Hajishirzi. 2020 · 2020
Cited alongside, same era.
Inquisitive question generation for high level text comprehension
Wei-Jen Ko, Te-yuan Chen, Yiyan Huang, Greg Durrett, and Junyi Jessy Li. 2020 · 2020
Cited alongside, same era.
On the stability of fine-tuning bert: Misconceptions, explanations, and strong baselines
Marius Mosbach, Maksym Andriushchenko, and Dietrich Klakow. 2021 · 2021
Closest in time.
Show your work: Scratchpads for intermediate computation with language models
Maxwell Nye, Anders Johan Andreassen, Guy Gur-Ari, Henryk Michalewski, Jacob Austin, David Bieber, David Dohan, Aitor Lewkowycz, Maarten Bosma, David Luan, Charles Sutton, and Augustus Odena. 2021 · 2021
Closest in time.
Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2021 · 2021
Closest in time.
How many data points is a prompt worth?
Teven Le Scao and Alexander M. Rush. 2021 · 2021
Closest in time.
General-purpose question-answering with Macaw
Oyvind Tafjord and Peter Clark. 2021 · 2021
Closest in time.
Generated knowledge prompting for commonsense reasoning
Jiachen Liu, Alisa Liu, Ximing Lu, Sean Welleck, Peter West, Ronan Le Bras, Yejin Choi, and Hannaneh Hajishirzi. 2022 · 2022
Closest in time.
Towards general natural language understanding with probabilistic worldbuilding
Abulhair Saparov and Tom. Mitchell. 2022 · 2022
Closest in time.
Contextualized scene imagination for generative commonsense reasoning
PeiFeng Wang, Jonathan Zamora, Junfeng Liu, Filip Ilievski, Muhao Chen, and Xiang Ren. 2022 · 2022
Closest in time.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou. 2022 · 2022
Closest in time.