Fetching the paper…
Reading the bibliography…
Despite large successes of recent language models on diverse tasks, they suffer from severe performance degeneration in low-resource settings with limited training data available.
Okapi at TREC-3
Stephen E. Robertson, Steve Walker, Susan Jones, Micheline Hancock-Beaulieu, and Mike Gatford. 1994 · 1994
Earlier work this paper cites.
Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
MS MARCO: A human generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
Earlier work this paper cites.
Data augmentation via dependency tree morphing for low-resource languages
Gözde Gül Sahin and Mark Steedman. 2018 · 2018
Earlier work this paper cites.
Teaching machines to ask questions
Kaichun Yao, Libo Zhang, Tiejian Luo, Lili Tao, and Yanjun Wu. 2018 · 2018
Earlier work this paper cites.
Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur P. 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
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019a · 2019
Earlier work this paper cites.
Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019b · 2019
Earlier work this paper cites.
EDA: easy data augmentation techniques for boosting performance on text classification tasks
Jason W. Wei and Kai Zou. 2019 · 2019
Earlier work this paper cites.
Policyqa: A reading comprehension dataset for privacy policies
Wasi Uddin Ahmad, Jianfeng Chi, Yuan Tian, and Kai-Wei Chang. 2020 · 2020
Earlier work this paper cites.
Do not have enough data? deep learning to the rescue!
Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, Naama Tepper, and Naama Zwerdling. 2020 · 2020
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
Earlier work this paper cites.
The techqa dataset
Vittorio Castelli, Rishav Chakravarti, Saswati Dana, Anthony Ferritto, Radu Florian, Martin Franz, Dinesh Garg, Dinesh Khandelwal, J. Scott McCarley, Mike McCawley, Mohamed Nasr, Lin Pan, Cezar Pendus, John F. Pitrelli, Saurabh Pujar, Salim Roukos, Andrzej Sakrajda, Avirup Sil, Rosario Uceda-Sosa, Todd Ward, and Rong Zhang. 2020 · 2020
Earlier work this paper cites.
Mixtext: Linguistically-informed interpolation of hidden space for semi-supervised text classification
Jiaao Chen, Zichao Yang, and Diyi Yang. 2020 · 2020
Earlier work this paper cites.
Sequence-level mixed sample data augmentation
Demi Guo, Yoon Kim, and Alexander M. Rush. 2020 · 2020
Earlier work this paper cites.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020 · 2020
Earlier work this paper cites.
Generating diverse and consistent QA pairs from contexts with information-maximizing hierarchical conditional vaes
Dong Bok Lee, Seanie Lee, Woo Tae Jeong, Donghwan Kim, and Sung Ju Hwang. 2020 · 2020
Earlier work this paper cites.
Covid-qa: A question answering dataset for covid-19
Timo Möller, Anthony Reina, Raghavan Jayakumar, and Malte Pietsch. 2020 · 2020
Cited alongside, same era.
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, and Peter J. Liu. 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, Rémi 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 M. Rush. 2020 · 2020
Cited alongside, same era.
Forget me not: Reducing catastrophic forgetting for domain adaptation in reading comprehension
Ying Xu, Xu Zhong, Antonio José Jimeno-Yepes, and Jey Han Lau. 2020 · 2020
Cited alongside, same era.
A survey of data augmentation approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard H. Hovy. 2021 · 2021
Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer. 2023 · 2023
Later among the works it cites.
The false promise of imitating proprietary llms
Arnav Gudibande, Eric Wallace, Charlie Snell, Xinyang Geng, Hao Liu, Pieter Abbeel, Sergey Levine, and Dawn Song. 2023 · 2023
Later among the works it cites.
Unnatural instructions: Tuning language models with (almost) no human labor
Or Honovich, Thomas Scialom, Omer Levy, and Timo Schick. 2023a · 2023
Later among the works it cites.
Unnatural instructions: Tuning language models with (almost) no human labor
Or Honovich, Thomas Scialom, Omer Levy, and Timo Schick. 2023b · 2023
Later among the works it cites.
Making large language models better data creators
Dong-Ho Lee, Jay Pujara, Mohit Sewak, Ryen White, and Sujay Kumar Jauhar. 2023 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2021 · 2021
Cited alongside, same era.
Efficiently teaching an effective dense retriever with balanced topic aware sampling
Sebastian Hofstätter, Sheng-Chieh Lin, Jheng-Hong Yang, Jimmy Lin, and Allan Hanbury. 2021 · 2021
Cited alongside, same era.
BEIR: A heterogenous benchmark for zero-shot evaluation of information retrieval models
Nandan Thakur, Nils Reimers, Andreas Rücklé, Abhishek Srivastava, and Iryna Gurevych. 2021 · 2021
Cited alongside, same era.
Inpars: Unsupervised dataset generation for information retrieval
Luiz Henrique Bonifacio, Hugo Queiroz Abonizio, Marzieh Fadaee, and Rodrigo Frassetto Nogueira. 2022 · 2022
Cited alongside, same era.
Improving in-context few-shot learning via self-supervised training
Mingda Chen, Jingfei Du, Ramakanth Pasunuru, Todor Mihaylov, Srini Iyer, Veselin Stoyanov, and Zornitsa Kozareva. 2022 · 2022
Cited alongside, same era.
Data augmentation approaches in natural language processing: A survey
Bohan Li, Yutai Hou, and Wanxiang Che. 2022 · 2022
Cited alongside, same era.
Metaicl: Learning to learn in context
Sewon Min, Mike Lewis, Luke Zettlemoyer, and Hannaneh Hajishirzi. 2022 · 2022
Cited alongside, same era.
Self-alignment with instruction backtranslation
Xian Li, Ping Yu, Chunting Zhou, Timo Schick, Luke Zettlemoyer, Omer Levy, Jason Weston, and Mike Lewis. 2023 · 2023
Later among the works it cites.
Domain specialization as the key to make large language models disruptive: A comprehensive survey
Chen Ling, Xujiang Zhao, Jiaying Lu, Chengyuan Deng, Can Zheng, Junxiang Wang, Tanmoy Chowdhury, Yun-Qing Li, Hejie Cui, Xuchao Zhang, Tian yu Zhao, Amit Panalkar, Wei Cheng, Haoyu Wang, Yanchi Liu, Zhengzhang Chen, Haifeng Chen, Chris White, Quanquan Gu, Jian Pei, Carl Yang, and Liang Zhao. 2023 · 2023
Later among the works it cites.
Adapt in contexts: Retrieval-augmented domain adaptation via in-context learning
Quanyu Long, Wenya Wang, and Sinno Jialin Pan. 2023 · 2023
Later among the works it cites.
Full parameter fine-tuning for large language models with limited resources
Kai Lv, Yuqing Yang, Tengxiao Liu, Qinghui Gao, Qipeng Guo, and Xipeng Qiu. 2023 · 2023
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. 2023 · 2023
Later among the works it cites.
OpenAI. 2023 · 2023
Later among the works it cites.
In-context retrieval-augmented language models
Ori Ram, Yoav Levine, Itay Dalmedigos, Dor Muhlgay, Amnon Shashua, Kevin Leyton-Brown, and Yoav Shoham. 2023 · 2023
Later among the works it cites.
UDAPDR: unsupervised domain adaptation via LLM prompting and distillation of rerankers
Jon Saad-Falcon, Omar Khattab, Keshav Santhanam, Radu Florian, Martin Franz, Salim Roukos, Avirup Sil, Md. Arafat Sultan, and Christopher Potts. 2023 · 2023
Later among the works it cites.
Can llms augment low-resource reading comprehension datasets? opportunities and challenges
Vinay Samuel, Houda Aynaou, Arijit Ghosh Chowdhury, Karthik Venkat Ramanan, and Aman Chadha. 2023 · 2023
Later among the works it cites.
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
Later among the works it cites.
Self-instruct: Aligning language models with self-generated instructions
Yizhong Wang, Yeganeh Kordi, Swaroop Mishra, Alisa Liu, Noah A. Smith, Daniel Khashabi, and Hannaneh Hajishirzi. 2023a · 2023
Later among the works it cites.
Llm-powered data augmentation for enhanced crosslingual performance
Chenxi Whitehouse, Monojit Choudhury, and Alham Fikri Aji. 2023 · 2023
Later among the works it cites.