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The emergent few-shot reasoning capabilities of Large Language Models (LLMs) have excited the natural language and machine learning community over recent years.
Recursive functions of symbolic expressions and their computation by machine, part i
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Adina Williams, Nikita Nangia, and SamuelR. Bowman · 2017
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Commonsenseqa: A question answering challenge targeting commonsense knowledge, Nov 2018
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2018
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Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Josh Tenenbaum · 2018
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh · 2021
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An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma · 2021
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Explanations for commonsenseqa: New dataset and models
Shourya Aggarwal, Divyanshu Mandowara, Vishwajeet Agrawal, Dinesh Khandelwal, Parag Singla, and Dinesh Garg · 2021
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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
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Case-based reasoning for natural language queries over knowledge bases
Rajarshi Das, Manzil Zaheer, Dung Thai, Ameya Godbole, Ethan Perez, Jay Yoon Lee, Lizhen Tan, Lazaros Polymenakos, and Andrew McCallum · 2021
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Improving neural program synthesis with inferred execution traces
Eui Chul Shin, Illia Polosukhin, and Dawn Song · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
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Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs, Mar 2019
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner · 2019
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Deep learning for symbolic mathematics
Guillaume Lample and François Charton · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
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, et al · 2020
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Exploiting cloze questions for few shot text classification and natural language inference
Timo Schick and Hinrich Schütze · 2020
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Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant · 2021
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
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What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou · 2022
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou · 2022
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Selective annotation makes language models better few-shot learners, 2022
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 · 2022
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Multitask prompted training enables zero-shot task generalization, 2022
Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Tali Bers, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M. Rush · 2022
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Do prompt-based models really understand the meaning of their prompts?
Albert Webson and Ellie Pavlick · 2022
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Ground-truth labels matter: A deeper look into input-label demonstrations
Junyeob Kim, Hyuhng Joon Kim, Hyunsoo Cho, Hwiyeol Jo, Sang-Woo Lee, Sang-goo Lee, Kang Min Yoo, and Taeuk Kim · 2022
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What learning algorithm is in-context learning? investigations with linear models, Nov 2022
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou · 2022
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Plansformer: Generating symbolic plans using transformers
Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan, Francesca Rossi, Lior Horesh, Biplav Srivastava, Francesco Fabiano, and Andrea Loreggia · 2022
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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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When do you need chain-of-thought prompting for chatgpt?
Jiuhai Chen, Lichang Chen, Heng Huang, and Tianyi Zhou · 2023
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