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Causal inference is one of the hallmarks of human intelligence.
RoBERTa: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 1907
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DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 1910
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Probabilistic reasoning in intelligent systems: Networks of plausible inference
Judea Pearl · 1988
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Causation, prediction, and search
Peter Spirtes, Clark Glymour, and Richard Scheines · 1993
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Causation, Prediction, and Search, Second Edition
Peter Spirtes, Clark Glymour, and Richard Scheines · 2000
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Optimal structure identification with greedy search
David Maxwell Chickering · 2002
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A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O. Hoyer, Aapo Hyvärinen, and Antti J. Kerminen · 2006
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Nonlinear causal discovery with additive noise models
Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, and Bernhard Schölkopf · 2008
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Modeling semantic containment and exclusion in natural language inference
Bill MacCartney and Christopher D. Manning · 2008
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Causality: Models, reasoning and inference (2nd ed.)
Judea Pearl · 2009
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Causality discovery with additive disturbances: An information-theoretical perspective
Kun Zhang and Aapo Hyvärinen · 2009
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Pre-trained summarization distillation
Sam Shleifer and Alexander M. Rush · 2010
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SemEval-2012 task 7: Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Andrew Gordon, Zornitsa Kozareva, and Melissa Roemmele · 2012
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Practical graph isomorphism, II
Brendan D. McKay and Adolfo Piperno · 2013
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning · 2015
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Causal inference in statistics: A primer
Madelyn Glymour, Judea Pearl, and Nicholas P Jewell · 2016
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Causal discovery and inference: Concepts and recent methodological advances
Peter Spirtes and Kun Zhang · 2016
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Elements of causal inference: Foundations and learning algorithms
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Review of causal discovery methods based on graphical models
Clark Glymour, Kun Zhang, and Peter Spirtes · 2019
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Counterfactual story reasoning and generation
Lianhui Qin, Antoine Bosselut, Ari Holtzman, Chandra Bhagavatula, Elizabeth Clark, and Yejin Choi · 2019
Reasoning about goals, steps, and temporal ordering with WikiHow
Li Zhang, Qing Lyu, and Chris Callison-Burch · 2020
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Deberta: Decoding-enhanced Bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen · 2021
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Multitask instruction-based prompting for fallacy recognition
Tariq Alhindi, Tuhin Chakrabarty, Elena Musi, and Smaranda Muresan · 2022
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Zhijing Jin, Abhinav Lalwani, Tejas Vaidhya, Xiaoyu Shen, Yiwen Ding, Zhiheng Lyu, Mrinmaya Sachan, Rada Mihalcea, and Bernhard Schölkopf · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe · 2022
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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ATOMIC: an atlas of machine commonsense for if-then reasoning
Maarten Sap, Ronan Le Bras, Emily Allaway, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A. Smith, and Yejin Choi · 2019
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Social iqa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi · 2019
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Abductive commonsense reasoning
Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, Keisuke Sakaguchi, Ari Holtzman, Hannah Rashkin, Doug Downey, Wen-tau Yih, and Yejin Choi · 2020
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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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris 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
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Is BERT really robust? A strong baseline for natural language attack on text classification and entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits · 2020
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OPT: open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona T. Diab, Xian Li, Xi Victoria Lin, Todor Mihaylov, Myle Ott, Sam Shleifer, Kurt Shuster, Daniel Simig, Punit Singh Koura, Anjali Sridhar, Tianlu Wang, and Luke Zettlemoyer · 2022
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Causal reasoning and large language models: Opening a new frontier for causality
Emre Kıcıman, Robert Ness, Amit Sharma, and Chenhao Tan · 2023
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OpenAI · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
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Causal-discovery performance of chatgpt in the context of neuropathic pain diagnosis
Ruibo Tu, Chao Ma, and Cheng Zhang · 2023
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Probing for correlations of causal facts: Large language models and causality, 2023
Moritz Willig, Matej Zečević, Devendra Singh Dhami, and Kristian Kersting · 2023
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Echo: Event causality inference via human-centric reasoning
Yuxi Xie, Guanzhen Li, and Min-Yen Kan · 2023
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Causal parrots: Large language models may talk causality but are not causal
Matej Zečević, Moritz Willig, Devendra Singh Dhami, and Kristian Kersting · 2023
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