Fetching the paper…
Reading the bibliography…
Microsoft Windows Feedback Hub is designed to receive customer feedback on a wide variety of subjects including critical topics such as power and battery.
The Curious Case of Neural Text Degeneration
Ari Holtzman, Jan Buys, Maxwell Forbes, and Yejin Choi. 2019 · 1904
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 · 2005
Earlier work this paper cites.
Causality (2 ed.)
Judea Pearl. 2009 · 2009
Earlier work this paper cites.
A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, Lisbon, Portugal, 632–642
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
Earlier work this paper cites.
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Guido W. Imbens and Donald B. Rubin. 2015 · 2015
Earlier work this paper cites.
Controlling Linguistic Style Aspects in Neural Language Generation. In Proceedings of the Workshop on Stylistic Variation . Association for Computational Linguistics, Copenhagen, Denmark, 94–104
Jessica Ficler and Yoav Goldberg. 2017 · 2017
Earlier work this paper cites.
Language Models are Unsupervised Multitask Learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments. In Neural Information Processing Systems
Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, and Greg Lewis. 2019 · 2019
Cited alongside, same era.
Scaling Laws for Neural Language Models
Jared Kaplan, Sam McCandlish, T. J. Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeff Wu, and Dario Amodei. 2020 · 2020
Cited alongside, same era.
DoWhy: An End-to-End Library for Causal Inference
Amit Sharma and Emre Kıcıman. 2020 · 2020
Cited alongside, same era.
Causal Reasoning and Large Language Models: Opening a New Frontier for Causality
Emre Kıcıman, Robert Osazuwa Ness, Amit Sharma, and Chenhao Tan. 2023 · 2023
Closest in time.
Can large language models build causal graphs?
Stephanie Long, Tibor Schuster, Alexandre Piché, Department of Family Medicine, McGill University, Mila, Université de Montreal, and ServiceNow Research. 2023 · 2023
Closest in time.
Assessing Sensitivity to Unconfoundedness: Estimation and Inference
Matthew Masten, Alexandre Poirier, and Linqi Zhang. 2023 · 2023
Closest in time.
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, Aur’elien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023 · 2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tomas Geffner, Javier Antorán, Adam Foster, Wenbo Gong, Chao Ma, Emre Kıcıman, Ajay Sharma, A. Lamb, Martin Kukla, Nick Pawlowski, Miltiadis Allamanis, and Cheng Zhang. 2022 · 2022
Cited alongside, same era.
A Causal AI Suite for Decision-Making. In NeurIPS 2022 Workshop on Causality for Real-world Impact
Emre Kiciman, Eleanor Dillon, Darren Edge, Adam Foster, Agrin Hilmkil, Joel Jennings, Chao Ma, Robert Osazuwa Ness, Nick Pawlowski, Amit Sharma, and Cheng Zhang. 2022 · 2022
Cited alongside, same era.
Finetuned Language Models are Zero-Shot Learners. In International Conference on Learning Representations
Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le. 2022a
Cited in the paper.
Chain of Thought Prompting Elicits Reasoning in Large Language Models. In Advances in Neural Information Processing Systems , Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (Eds.)
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed H. Chi, Quoc V Le, and Denny Zhou. 2022b
Cited in the paper.
Self-Consistency Improves Chain of Thought Reasoning in Language Models. In The Eleventh International Conference on Learning Representations
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2023 · 2023
Closest in time.