Fetching the paper…
Reading the bibliography…
While in-context learning is commonly associated with causal language models, such as GPT, we demonstrate that this capability also 'emerges' in masked language models.
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
Earlier work this paper cites.
Megatron-LM: Training multi-billion parameter language models using model parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro. 2019 · 1909
Earlier work this paper cites.
Acquiring a single new word
Susan Carey and Elsa Bartlett. 1978 · 1978
Earlier work this paper cites.
The second PASCAL recognising textual entailment challenge
Roy Bar-Haim, Ido Dagan, Bill Dolan, Lisa Ferro, and Danilo Giampiccolo. 2006 · 2006
Earlier work this paper cites.
The PASCAL recognising textual entailment challenge
Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
Earlier work this paper cites.
The third PASCAL recognizing textual entailment challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007 · 2007
Earlier work this paper cites.
Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Bejan, and Andrew Gordon. 2011 · 2011
Earlier work this paper cites.
The Winograd schema challenge
Hector J. Levesque, Ernest Davis, and Leora Morgenstern. 2012 · 2012
Earlier work this paper cites.
Semantic parsing on Freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
Earlier work this paper cites.
Findings of the 2014 workshop on statistical machine translation
Ondřej Bojar, Christian Buck, Christian Federmann, Barry Haddow, Philipp Koehn, Johannes Leveling, Christof Monz, Pavel Pecina, Matt Post, Herve Saint-Amand, Radu Soricut, Lucia Specia, and Aleš Tamchyna. 2014 · 2014
Earlier work this paper cites.
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
Earlier work this paper cites.
Findings of the 2016 conference on machine translation
Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Martin Popel, Matt Post, Raphael Rubino, Carolina Scarton, Lucia Specia, Marco Turchi, Karin Verspoor, and Marcos Zampieri. 2016 · 2016
Earlier work this paper cites.
A corpus and cloze evaluation for deeper understanding of commonsense stories
Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, and James Allen. 2016 · 2016
Earlier work this paper cites.
The LAMBADA dataset: Word prediction requiring a broad discourse context
Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. 2016 · 2016
Earlier work this paper cites.
TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
Earlier work this paper cites.
Think you have solved question answering? Try ARC, the AI2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018 · 2018
Earlier work this paper cites.
Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018 · 2018
Earlier work this paper cites.
Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
Earlier work this paper cites.
A call for clarity in reporting BLEU scores
Matt Post. 2018 · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. 2018 · 2018
Earlier work this paper cites.
ReCoRD: Bridging the gap between human and machine commonsense reading comprehension
Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme. 2018 · 2018
Earlier work this paper cites.
BoolQ: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 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.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 2019
Cited alongside, same era.
OpenWebText corpus
Aaron Gokaslan and Vanya Cohen. 2019 · 2019
Cited alongside, same era.
Natural questions: A benchmark for question answering research
Larger-scale transformers for multilingual masked language modeling
Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, and Alexis Conneau. 2021 · 2021
Later among the works it cites.
DeBERTa: Decoding-enhanced BERT with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2021 · 2021
Later among the works it cites.
A mathematical exploration of why language models help solve downstream tasks
Nikunj Saunshi, Sadhika Malladi, and Sanjeev Arora. 2021 · 2021
Later among the works it cites.
It’s not just size that matters: Small language models are also few-shot learners
Timo Schick and Hinrich Schütze. 2021 · 2021
Later among the works it cites.
CM3: A causal masked multimodal model of the internet
Armen Aghajanyan, Bernie Huang, Candace Ross, Vladimir Karpukhin, Hu Xu, Naman Goyal, Dmytro Okhonko, Mandar Joshi, Gargi Ghosh, Mike Lewis, and Luke Zettlemoyer. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, Kristina Toutanova, Llion Jones, Matthew Kelcey, Ming-Wei Chang, Andrew M. Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov. 2019 · 2019
Cited alongside, same era.
WiC: the word-in-context dataset for evaluating context-sensitive meaning representations
Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019 · 2019
Cited alongside, same era.
A simple method for commonsense reasoning
Trieu H. Trinh and Quoc V. Le. 2019 · 2019
Cited alongside, same era.
BERT has a mouth, and it must speak: BERT as a Markov random field language model
Alex Wang and Kyunghyun Cho. 2019 · 2019
Cited alongside, same era.
SuperGLUE: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2019 · 2019
Cited alongside, same era.
HellaSwag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
Cited alongside, same era.
PIQA: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Ronan Le bras, Jianfeng Gao, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
GLM: General language model pretraining with autoregressive blank infilling
Zhengxiao Du, Yujie Qian, Xiao Liu, Ming Ding, Jiezhong Qiu, Zhilin Yang, and Jie Tang. 2022 · 2022
Later among the works it cites.
Few-shot learning with multilingual generative language models
Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, and Xian Li. 2022 · 2022
Later among the works it cites.
Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
Haokun Liu, Derek Tam, Muqeeth Mohammed, Jay Mohta, Tenghao Huang, Mohit Bansal, and Colin Raffel. 2022 · 2022
Later among the works it cites.
In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. 2022 · 2022
Later among the works it cites.
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, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022 · 2022
Later among the works it cites.
Prompting ELECTRA: Few-shot learning with discriminative pre-trained models
Mengzhou Xia, Mikel Artetxe, Jingfei Du, Danqi Chen, and Veselin Stoyanov. 2022 · 2022
Later among the works it cites.
OPT: Open pre-trained transformer language models
Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen, Christopher Dewan, Mona 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 · 2022
Later among the works it cites.
The unreasonable effectiveness of few-shot learning for machine translation
Xavier Garcia, Yamini Bansal, Colin Cherry, George Foster, Maxim Krikun, Melvin Johnson, and Orhan Firat. 2023 · 2023
Later among the works it cites.
Demystifying prompts in language models via perplexity estimation
Hila Gonen, Srini Iyer, Terra Blevins, Noah Smith, and Luke Zettlemoyer. 2023 · 2023
Later among the works it cites.
A better way to do masked language model scoring
Carina Kauf and Anna Ivanova. 2023 · 2023
Later among the works it cites.
Scaling data-constrained language models
Niklas Muennighoff, Alexander M Rush, Boaz Barak, Teven Le Scao, Nouamane Tazi, Aleksandra Piktus, Sampo Pyysalo, Thomas Wolf, and Colin Raffel. 2023 · 2023
Later among the works it cites.
UL2: Unifying language learning paradigms
Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Jason Wei, Xuezhi Wang, Hyung Won Chung, Dara Bahri, Tal Schuster, Steven Zheng, Denny Zhou, Neil Houlsby, and Donald Metzler. 2023 · 2023
Later among the works it cites.
Large language models are latent variable models: Explaining and finding good demonstrations for in-context learning
Xinyi Wang, Wanrong Zhu, Michael Saxon, Mark Steyvers, and William Yang Wang. 2023 · 2023
Later among the works it cites.
CausalLM is not optimal for in-context learning
Nan Ding, Tomer Levinboim, Jialin Wu, Sebastian Goodman, and Radu Soricut. 2024 · 2024
Closest in time.
RULER: What’s the real context size of your long-context language models?
Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, Shantanu Acharya, Dima Rekesh, Fei Jia, Yang Zhang, and Boris Ginsburg. 2024 · 2024
Closest in time.
More room for language: Investigating the effect of retrieval on language models
David Samuel, Lucas Georges Gabriel Charpentier, and Sondre Wold. 2024 · 2024
Closest in time.