Fetching the paper…
Reading the bibliography…
Utilizing language models (LMs) without internal access is becoming an attractive paradigm in the field of NLP as many cutting-edge LMs are released through APIs and boast a massive scale.
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 · 1901
Earlier work this paper cites.
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.
Wordnet: a lexical database for english
George A Miller. 1995 · 1995
Earlier work this paper cites.
Efficient intent detection with dual sentence encoders
Iñigo Casanueva, Tadas Temčinas, Daniela Gerz, Matthew Henderson, and Ivan Vulić. 2020 · 2003
Earlier work this paper cites.
Learning word vectors for sentiment analysis
Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. 2011 · 2011
Earlier work this paper cites.
Hidden factors and hidden topics: understanding rating dimensions with review text
Julian McAuley and Jure Leskovec. 2013 · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015 · 2015
Earlier work this paper cites.
Dbpedia abstracts: a large-scale, open, multilingual nlp training corpus
Martin Brümmer, Milan Dojchinovski, and Sebastian Hellmann. 2016 · 2016
Earlier work this paper cites.
Conceptnet 5.5: An open multilingual graph of general knowledge
Robyn Speer, Joshua Chin, and Catherine Havasi. 2017 · 2017
Earlier work this paper cites.
An evaluation dataset for intent classification and out-of-scope prediction
Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, and Jason Mars. 2019 · 2019
Earlier work this paper cites.
Unsupervised domain clusters in pretrained language models
Roee Aharoni and Yoav Goldberg. 2020 · 2020
Earlier work this paper cites.
Text classification using label names only: A language model self-training approach
Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, and Jiawei Han. 2020 · 2020
Earlier work this paper cites.
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, Peter J Liu, et al. 2020 · 2020
Cited alongside, same era.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush. 2020 · 2020
Cited alongside, same era.
Openprompt: An open-source framework for prompt-learning
Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Hai-Tao Zheng, and Maosong Sun. 2021 · 2021
Cited alongside, same era.
Beyond prompting: Making pre-trained language models better zero-shot learners by clustering representations
Yuxiao Fei, Ping Nie, Zhao Meng, Roger Wattenhofer, and Mrinmaya Sachan. 2022b · 2021
Cited alongside, same era.
Simcse: Simple contrastive learning of sentence embeddings
Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
Later among the works it cites.
Black-box prompt learning for pre-trained language models
Shizhe Diao, Xuechun Li, Yong Lin, Zhichao Huang, and Tong Zhang. 2022 · 2022
Later among the works it cites.
Jimin Hong, Jungsoo Park, Daeyoung Kim, Seongjae Choi, Bokyung Son, and Jaewook Kang. 2022 · 2022
Later among the works it cites.
Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification
Shengding Hu, Ning Ding, Huadong Wang, Zhiyuan Liu, Jingang Wang, Juanzi Li, Wei Wu, and Maosong Sun. 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…
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification
Shengding Hu, Ning Ding, Huadong Wang, Zhiyuan Liu, Juanzi Li, and Maosong Sun. 2021 · 2021
Cited alongside, same era.
True few-shot learning with language models
Ethan Perez, Douwe Kiela, and Kyunghyun Cho. 2021 · 2021
Cited alongside, same era.
Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2021 · 2021
Cited alongside, same era.
Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2021 · 2021
Cited alongside, same era.
Ernie 3.0: Large-scale knowledge enhanced pre-training for language understanding and generation
Yu Sun, Shuohuan Wang, Shikun Feng, Siyu Ding, Chao Pang, Junyuan Shang, Jiaxiang Liu, Xuyi Chen, Yanbin Zhao, Yuxiang Lu, et al. 2021 · 2021
Cited alongside, same era.
X-class: Text classification with extremely weak supervision
Zihan Wang, Dheeraj Mekala, and Jingbo Shang. 2021 · 2021
Cited alongside, same era.
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022 · 2022
Later among the works it cites.
Z-icl: Zero-shot in-context learning with pseudo-demonstrations
Xinxi Lyu, Sewon Min, Iz Beltagy, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022 · 2022
Later among the works it cites.
Noisy channel language model prompting for few-shot text classification
Sewon Min, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
Later among the works it cites.
Probabilistic machine learning: an introduction
Kevin P Murphy. 2022 · 2022
Later among the works it cites.
Bloom: A 176b-parameter open-access multilingual language model
Teven Le Scao, Angela Fan, Christopher Akiki, Ellie Pavlick, Suzana Ilić, Daniel Hesslow, Roman Castagné, Alexandra Sasha Luccioni, François Yvon, Matthias Gallé, et al. 2022 · 2022
Later among the works it cites.
Nearest neighbor zero-shot inference
Weijia Shi, Julian Michael, Suchin Gururangan, and Luke Zettlemoyer. 2022 · 2022
Later among the works it cites.
Black-box tuning for language-model-as-a-service
Tianxiang Sun, Yunfan Shao, Hong Qian, Xuanjing Huang, and Xipeng Qiu. 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, et al. 2022 · 2022
Later among the works it cites.
Prompt-augmented linear probing: Scaling beyond the limit of few-shot in-context learners
Hyunsoo Cho, Hyuhng Joon Kim, Junyeob Kim, Sang-woo Lee, Sang-goo Lee, Kang Min Yoo, and Taeuk Kim. 2023 · 2023
Closest in time.