Fetching the paper…
Reading the bibliography…
Large language models (LMs) beyond a certain scale, demonstrate the emergent capability of generating free-text rationales for their predictions via chain-of-thought (CoT) prompting.
Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 1905
Earlier work this paper cites.
On faithfulness and factuality in abstractive summarization
Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020 · 1919
Earlier work this paper cites.
Counterfactual thinking
Neal J Roese. 1997 · 1997
Earlier work this paper cites.
Language (technology) is power: A critical survey of" bias" in nlp
Su Lin Blodgett, Solon Barocas, Hal Daumé III, and Hanna Wallach. 2020 · 2005
Earlier work this paper cites.
Peter Hase, Shiyue Zhang, Harry Xie, and Mohit Bansal. 2020 · 2010
Earlier work this paper cites.
e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom. 2018 · 2018
Earlier work this paper cites.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R Bowman, and Noah A Smith. 2018 · 2018
Earlier work this paper cites.
Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2018 · 2018
Earlier work this paper cites.
Explain yourself! leveraging language models for commonsense reasoning
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 2019
Earlier work this paper cites.
QASC: A dataset for question answering via sentence composition
Tushar Khot, Peter Clark, Michal Guerquin, Peter Jansen, and Ashish Sabharwal. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
Cited alongside, same era.
Explanations for commonsenseqa: New dataset and models
Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, and Dinesh Garg. 2021 · 2021
Cited alongside, same era.
Shortcutted commonsense: Data spuriousness in deep learning of commonsense reasoning
Ruben Branco, António Branco, Joao Rodrigues, and Joao Silva. 2021 · 2021
Cited alongside, same era.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021 · 2021
Cited alongside, same era.
Reframing human-ai collaboration for generating free-text explanations
Sarah Wiegreffe, Jack Hessel, Swabha Swayamdipta, Mark Riedl, and Yejin Choi. 2021 · 2021
Later among the works it cites.
GPT-NeoX-20B: An open-source autoregressive language model
Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, and Samuel Weinbach. 2022 · 2022
Later among the works it cites.
Knife: Knowledge distillation with free-text rationales
Aaron Chan, Zhiyuan Zeng, Wyatt Lake, Brihi Joshi, Hanjie Chen, and Xiang Ren. 2022 · 2022
Later among the works it cites.
Jacob Eisenstein, Daniel Andor, Bernd Bohnet, Michael Collins, and David Mimno. 2022 · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
Gender and representation bias in GPT-3 generated stories
Li Lucy and David Bamman. 2021 · 2021
Cited alongside, same era.
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, et al. 2021 · 2021
Cited alongside, same era.
Creak: A dataset for commonsense reasoning over entity knowledge
Yasumasa Onoe, Michael J.Q. Zhang, Eunsol Choi, and Greg Durrett. 2021 · 2021
Cited alongside, same era.
Explanations from large language models make small reasoners better
Shiyang Li, Jianshu Chen, Yelong Shen, Zhiyu Chen, Xinlu Zhang, Zekun Li, Hong Wang, Jing Qian, Baolin Peng, Yi Mao, et al. 2022a
Cited in the paper.
Contrastive decoding: Open-ended text generation as optimization
Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, and Mike Lewis. 2022b
Cited in the paper.
Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. 2022a
Cited in the paper.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, and Pascale Fung. 2022 · 2022
Later among the works it cites.
Kumar Shridhar, Alessandro Stolfo, and Mrinmaya Sachan. 2022 · 2022
Later among the works it cites.
Investigating the benefits of free-form rationales
Jiao Sun, Swabha Swayamdipta, Jonathan May, and Xuezhe Ma. 2022 · 2022
Later among the works it cites.
Pinto: Faithful language reasoning using prompt-generated rationales
Peifeng Wang, Aaron Chan, Filip Ilievski, Muhao Chen, and Xiang Ren. 2022 · 2022
Later among the works it cites.
The unreliability of explanations in few-shot in-context learning
Xi Ye and Greg Durrett. 2022 · 2022
Later among the works it cites.