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In-context learning, where pre-trained language models learn to perform tasks from task examples and instructions in their contexts, has attracted much attention in the NLP community.
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. 2019 · 1907
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Natural language understanding with the quora question pairs dataset
Lakshay Sharma, Laura Graesser, Nikita Nangia, and Utku Evci. 2019 · 1907
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001
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ROUGE: A package for automatic evaluation of summaries
Chin-Yew Lin. 2004 · 2004
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A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts
Bo Pang and Lillian Lee. 2004 · 2004
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Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
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Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
Bo Pang and Lillian Lee. 2005 · 2005
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The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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Dbpedia - a large-scale, multilingual knowledge base extracted from wikipedia
Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Sören Auer, and Christian Bizer. 2014 · 2014
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WikiQA: A challenge dataset for open-domain question answering
Yi Yang, Wen-tau Yih, and Christopher Meek. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
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Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
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Neural text generation from structured data with application to the biography domain
Rémi Lebret, David Grangier, and Michael Auli. 2016 · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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“liar, liar pants on fire”: A new benchmark dataset for fake news detection
William Yang Wang. 2017 · 2017
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Crowdsourcing multiple choice science questions
Johannes Welbl, Nelson F. Liu, and Matt Gardner. 2017 · 2017
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Identifying well-formed natural language questions
Manaal Faruqui and Dipanjan Das. 2018 · 2018
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Scitail: A textual entailment dataset from science question answering
Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018 · 2018
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Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization
Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018 · 2018
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Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
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Tackling adversarial examples in QA via answer sentence selection
Yuanhang Ren, Ye Du, and Di Wang. 2018 · 2018
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DuoRC: Towards complex language understanding with paraphrased reading comprehension
Amrita Saha, Rahul Aralikatte, Mitesh M. Khapra, and Karthik Sankaranarayanan. 2018 · 2018
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Constructing datasets for multi-hop reading comprehension across documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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SWAG: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018
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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. 2019 · 2019
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Tabfact: A large-scale dataset for table-based fact verification
Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou, and William Yang Wang. 2019 · 2019
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Quoref: A reading comprehension dataset with questions requiring coreferential reasoning
Pradeep Dasigi, Nelson F. Liu, Ana Marasović, Noah A. Smith, and Matt Gardner. 2019 · 2019
Cited alongside, same era.
The CommitmentBank: Investigating projection in naturally occurring discourse
Marie-Catherine De Marneffe, Mandy Simons, and Judith Tonhauser. 2019 · 2019
Cited alongside, same era.
SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019 · 2019
Cited alongside, same era.
Openwebtext corpus
Aaron Gokaslan, Vanya Cohen, Ellie Pavlick, and Stefanie Tellex. 2019 · 2019
Cited alongside, same era.
Cosmos QA: Machine reading comprehension with contextual commonsense reasoning
Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
FreebaseQA: A new factoid QA data set matching trivia-style question-answer pairs with Freebase
GPT-Neo: Large Scale Autoregressive Language Modeling with Mesh-Tensorflow
Sid Black, Gao Leo, Phil Wang, Connor Leahy, and Stella Biderman. 2021 · 2021
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A mathematical framework for transformer circuits
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, et al. 2021 · 2021
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Pre-trained models: Past, present and future
Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, et al. 2021 · 2021
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Surface form competition: Why the highest probability answer isn’t always right
Ari Holtzman, Peter West, Vered Shwartz, Yejin Choi, and Luke Zettlemoyer. 2021 · 2021
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The inductive bias of in-context learning: Rethinking pretraining example design
Yoav Levine, Noam Wies, Daniel Jannai, Dan Navon, Yedid Hoshen, and Amnon Shashua. 2021 · 2021
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Kelvin Jiang, Dekun Wu, and Hui Jiang. 2019 · 2019
Cited alongside, same era.
Billion-scale similarity search with gpus
Jeff Johnson, Matthijs Douze, and Hervé Jégou. 2019 · 2019
Cited alongside, same era.
Reasoning over paragraph effects in situations
Kevin Lin, Oyvind Tafjord, Peter Clark, and Matt Gardner. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Cited alongside, same era.
Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
Social IQa: Commonsense reasoning about social interactions
Maarten Sap, Hannah Rashkin, Derek Chen, Ronan Le Bras, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
Mining discourse markers for unsupervised sentence representation learning
Damien Sileo, Tim Van De Cruys, Camille Pradel, and Philippe Muller. 2019 · 2019
Cited alongside, same era.
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 2021
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GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Ben Wang and Aran Komatsuzaki. 2021 · 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 · 2021
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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 · 2021
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PromptSource: An integrated development environment and repository for natural language prompts
Stephen H Bach, Victor Sanh, Zheng-Xin Yong, Albert Webson, Colin Raffel, Nihal V Nayak, et al. 2022 · 2022
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Data distributional properties drive emergent in-context learning in transformers
Stephanie CY Chan, Adam Santoro, Andrew Kyle Lampinen, Jane X Wang, Aaditya K Singh, Pierre Harvey Richemond, James McClelland, and Felix Hill. 2022 · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Wikimedia downloads
Wikimedia Foundation. 2022 · 2022
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What can transformers learn in-context? a case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy Liang, and Gregory Valiant. 2022 · 2022
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Prototypical calibration for few-shot learning of language models
Zhixiong Han, Yaru Hao, Li Dong, Yutao Sun, and Furu Wei. 2022 · 2022
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Structured prompting: Scaling in-context learning to 1,000 examples
Yaru Hao, Yutao Sun, Li Dong, Zhixiong Han, Yuxian Gu, and Furu Wei. 2022 · 2022
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What makes good in-context examples for GPT-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2022 · 2022
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022 · 2022
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Cross-task generalization via natural language crowdsourcing instructions
Swaroop Mishra, Daniel Khashabi, Chitta Baral, and Hannaneh Hajishirzi. 2022 · 2022
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In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, et al. 2022 · 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, et al. 2022 · 2022
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Impact of pretraining term frequencies on few-shot reasoning
Yasaman Razeghi, Robert L Logan IV, Matt Gardner, and Sameer Singh. 2022 · 2022
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Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2022 · 2022
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, et al. 2022 · 2022
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On the effect of pretraining corpora on in-context learning by a large-scale language model
Seongjin Shin, Sang-Woo Lee, Hwijeen Ahn, Sungdong Kim, HyoungSeok Kim, Boseop Kim, Kyunghyun Cho, Gichang Lee, Woomyoung Park, Jung-Woo Ha, and Nako Sung. 2022 · 2022
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Benchmarking generalization via in-context instructions on 1,600+ language tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, et al. 2022 · 2022
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2022 · 2022
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