Fetching the paper…
Reading the bibliography…
Large language models (LLMs) enable in-context learning (ICL) by conditioning on a few labeled training examples as a text-based prompt, eliminating the need for parameter updates and achieving competitive performance.
ERNIE: enhanced representation through knowledge integration
Yu Sun, Shuohuan Wang, Yu-Kun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, and Hua Wu. 2019 · 1904
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.
Building a question answering test collection
Ellen M. Voorhees and Dawn M. Tice. 2000 · 2000
Earlier work this paper cites.
Scalable training of L1-regularized log-linear models
Galen Andrew and Jianfeng Gao. 2007 · 2007
Earlier work this paper cites.
Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
TAGME: on-the-fly annotation of short text fragments (by wikipedia entities)
Paolo Ferragina and Ugo Scaiella. 2010 · 2010
Earlier work this paper cites.
Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
Earlier work this paper cites.
Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
Earlier work this paper cites.
Improving language understanding by generative pre-training
Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018 · 2018
Earlier work this paper cites.
Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
Earlier work this paper cites.
Quoref: A reading comprehension dataset with questions requiring coreferential reasoning
Pradeep Dasigi, Nelson F. Liu, Ana Marasovic, Noah A. Smith, and Matt Gardner. 2019 · 2019
Earlier work this paper cites.
BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
Earlier work this paper cites.
Quartz: An open-domain dataset of qualitative relationship questions
Oyvind Tafjord, Matt Gardner, Kevin Lin, and Peter Clark. 2019 · 2019
Earlier work this paper cites.
Commonsenseqa: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant. 2019 · 2019
Earlier work this paper cites.
Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019 · 2019
Earlier work this paper cites.
ERNIE: enhanced language representation with informative entities
Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. 2019 · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Cited alongside, same era.
ALBERT: A lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020 · 2020
Cited alongside, same era.
BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
K-BERT: enabling language representation with knowledge graph
Weijie Liu, Peng Zhou, Zhe Zhao, Zhiruo Wang, Qi Ju, Haotang Deng, and Ping Wang. 2020 · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Learning to retrieve prompts for in-context learning
Ohad Rubin, Jonathan Herzig, and Jonathan Berant. 2022 · 2022
Later among the works it cites.
Prompting GPT-3 to be reliable
Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan L. Boyd-Graber, and Lijuan Wang. 2022 · 2022
Later among the works it cites.
Galactica: A large language model for science
Ross Taylor, Marcin Kardas, Guillem Cucurull, Thomas Scialom, Anthony Hartshorn, Elvis Saravia, Andrew Poulton, Viktor Kerkez, and Robert Stojnic. 2022 · 2022
Later among the works it cites.
Knowledge prompting in pre-trained language model for natural language understanding
Jianing Wang, Wenkang Huang, Qiuhui Shi, Hongbin Wang, Minghui Qiu, Xiang Li, and Ming Gao. 2022 · 2022
Later among the works it cites.
Chain of thought prompting elicits reasoning in large language models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021 · 2021
Cited alongside, same era.
PPT: pre-trained prompt tuning for few-shot learning
Yuxian Gu, Xu Han, Zhiyuan Liu, and Minlie Huang. 2021 · 2021
Cited alongside, same era.
Deberta: decoding-enhanced bert with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen. 2021 · 2021
Cited alongside, same era.
A survey of knowledge enhanced pre-trained models
Jian Yang, Gang Xiao, Yulong Shen, Wei Jiang, Xinyu Hu, Ying Zhang, and Jinghui Peng. 2021 · 2021
Cited alongside, same era.
Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 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, Jingang Wang, Juanzi Li, Wei Wu, and Maosong Sun. 2022 · 2022
Cited alongside, same era.
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 · 2022
Cited alongside, same era.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed H. Chi, Quoc Le, and Denny Zhou. 2022 · 2022
Later among the works it cites.
Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, Aviya Skowron, Lintang Sutawika, and Oskar van der Wal. 2023 · 2023
Closest in time.
Self-icl: Zero-shot in-context learning with self-generated demonstrations
Wei-Lin Chen, Cheng-Kuang Wu, Yun-Nung Chen, and Hsin-Hsi Chen. 2023 · 2023
Closest in time.
Chain-of-verification reduces hallucination in large language models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. 2023 · 2023
Closest in time.
A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, and Zhifang Sui. 2023 · 2023
Closest in time.
Prompting large language models with chain-of-thought for few-shot knowledge base question generation
Yuanyuan Liang, Jianing Wang, Hanlun Zhu, Lei Wang, Weining Qian, and Yunshi Lan. 2023 · 2023
Closest in time.
Z-ICL: zero-shot in-context learning with pseudo-demonstrations
Xinxi Lyu, Sewon Min, Iz Beltagy, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2023 · 2023
Closest in time.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton-Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurélien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom. 2023 · 2023
Closest in time.
Openicl: An open-source framework for in-context learning
Zhenyu Wu, Yaoxiang Wang, Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Jingjing Xu, and Yu Qiao. 2023 · 2023
Closest in time.
Making pre-trained language models end-to-end few-shot learners with contrastive prompt tuning
Ziyun Xu, Chengyu Wang, Minghui Qiu, Fuli Luo, Runxin Xu, Songfang Huang, and Jun Huang. 2023 · 2023
Closest in time.
From complex to simple: Unraveling the cognitive tree for reasoning with small language models
Junbing Yan, Chengyu Wang, Taolin Zhang, Xiaofeng He, Jun Huang, and Wei Zhang. 2023 · 2023
Closest in time.
Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola. 2023 · 2023
Closest in time.