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Existing research on Domain Robustness (DR) suffers from disparate setups, limited task variety, and scarce research on recent capabilities such as in-context learning.
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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Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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A survey on domain adaptation theory
Ievgen Redko, Emilie Morvant, Amaury Habrard, Marc Sebban, and Younès Bennani. 2020 · 2004
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Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010 · 2010
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LEGAL-BERT: the muppets straight out of law school
Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. 2020 · 2010
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Using domain similarity for performance estimation
Vincent Van Asch and Walter Daelemans. 2010 · 2010
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Effective measures of domain similarity for parsing
Barbara Plank and Gertjan van Noord. 2011 · 2011
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Domain adaptation using domain similarity- and domain complexity-based instance selection for cross-domain sentiment analysis
Robert Remus. 2012 · 2012
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Semeval-2014 task 4: Aspect based sentiment analysis
Maria Pontiki, Dimitris Galanis, John Pavlopoulos, Harris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. 2014 · 2014
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The airline review dataset
Quang Nguyen. 2015 · 2015
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Semeval-2015 task 12: Aspect based sentiment analysis
Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Suresh Manandhar, and Ion Androutsopoulos. 2015 · 2015
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Semeval-2016 task 5: Aspect based sentiment analysis
Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, and et al. Suresh Manandhar. 2016 · 2016
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Squad: 100, 000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Data selection strategies for multi-domain sentiment analysis
Sebastian Ruder, Parsa Ghaffari, and John G. Breslin. 2017 · 2017
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Tl;dr: Mining reddit to learn automatic summarization
Michael Völske, Martin Potthast, Shahbaz Syed, and Benno Stein. 2017 · 2017
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Neural structural correspondence learning for domain adaptation
Yftah Ziser and Roi Reichart. 2017 · 2017
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Synthetic and natural noise both break neural machine translation
Yonatan Belinkov and Yonatan Bisk. 2018 · 2018
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Multiwoz - A large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling
Pawel Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, and Milica Gasic. 2018 · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 2018 · 2018
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Pivot based language modeling for improved neural domain adaptation
Yftah Ziser and Roi Reichart. 2018 · 2018
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To annotate or not? predicting performance drop under domain shift
Hady ElSahar and Matthias Gallé. 2019 · 2019
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Unsupervised domain adaptation of contextualized embeddings for sequence labeling
Xiaochuang Han and Jacob Eisenstein. 2019 · 2019
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A challenge dataset and effective models for aspect-based sentiment analysis
Qingnan Jiang, Lei Chen, Ruifeng Xu, Xiang Ao, and Min Yang. 2019 · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Deep contextualized self-training for low resource dependency parsing
Guy Rotman and Roi Reichart. 2019 · 2019
Earlier work this paper cites.
Models in the wild: On corruption robustness of neural NLP systems
Barbara Rychalska, Dominika Basaj, Alicja Gosiewska, and Przemyslaw Biecek. 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 R. Bowman. 2019 · 2019
Cited alongside, same era.
Task refinement learning for improved accuracy and stability of unsupervised domain adaptation
Yftah Ziser and Roi Reichart. 2019 · 2019
Cited alongside, same era.
PERL: pivot-based domain adaptation for pre-trained deep contextualized embedding models
Eyal Ben-David, Carmel Rabinovitz, and Roi Reichart. 2020 · 2020
Cited alongside, same era.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, and et al. Jared Kaplan. 2020 · 2020
Cited alongside, same era.
Data contamination: From memorization to exploitation
Inbal Magar and Roy Schwartz. 2022 · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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M2D2: A massively multi-domain language modeling dataset
Machel Reid, Victor Zhong, Suchin Gururangan, and Luke Zettlemoyer. 2022 · 2022
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Towards improving selective prediction ability of NLP systems
Neeraj Varshney, Swaroop Mishra, and Chitta Baral. 2022 · 2022
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Example-based hypernetworks for out-of-distribution generalization
Tomer Volk, Eyal Ben-David, Ohad Amosy, Gal Chechik, and Roi Reichart. 2022 · 2022
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasovic, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
Cited alongside, same era.
Pretrained transformers improve out-of-distribution robustness
Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, and Dawn Song. 2020 · 2020
Cited alongside, same era.
Is BERT really robust? A strong baseline for natural language attack on text classification and entailment
Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020 · 2020
Cited alongside, same era.
Learning the difference that makes A difference with counterfactually-augmented data
Divyansh Kaushik, Eduard H. Hovy, and Zachary Chase Lipton. 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.
The effect of natural distribution shift on question answering models
John Miller, Karl Krauth, Benjamin Recht, and Ludwig Schmidt. 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.
What language model architecture and pretraining objective works best for zero-shot generalization?
Thomas Wang, Adam Roberts, Daniel Hesslow, Teven Le Scao, Hyung Won Chung, Iz Beltagy, Julien Launay, and Colin Raffel. 2022a · 2022
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Measure and improve robustness in NLP models: A survey
Xuezhi Wang, Haohan Wang, and Diyi Yang. 2022b · 2022
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Measuring robustness for NLP
Yu Yu, Abdul Rafae Khan, and Jia Xu. 2022 · 2022
Later among the works it cites.
Nitay Calderon, Subhabrata Mukherjee, Roi Reichart, and Amir Kantor. 2023 · 2023
Closest in time.
Faithful explanations of black-box NLP models using llm-generated counterfactuals
Yair Ori Gat, Nitay Calderon, Amir Feder, Alexander Chapanin, Amit Sharma, and Roi Reichart. 2023 · 2023
Closest in time.
Domain adaptation via prompt learning
Chunjiang Ge, Rui Huang, Mixue Xie, Zihang Lai, Shiji Song, Shuang Li, and Gao Huang. 2023 · 2023
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A taxonomy and review of generalization research in NLP
Dieuwke Hupkes, Mario Giulianelli, Verna Dankers, Mikel Artetxe, Yanai Elazar, Tiago Pimentel, Christos E. Christodoulopoulos, Karim Lasri, Naomi Saphra, Arabella Sinclair, Dennis Ulmer, Florian Schottmann, Khuyagbaatar Batsuren, Kaiser Sun, Koustuv Sinha, Leila Khalatbari, Maria Ryskina, Rita Frieske, Ryan Cotterell, and Zhijing Jin. 2023 · 2023
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Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de Las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed. 2023 · 2023
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A survey on out-of-distribution detection in NLP
Hao Lang, Yinhe Zheng, Yixuan Li, Jian Sun, Fei Huang, and Yongbin Li. 2023 · 2023
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A comprehensive survey on test-time adaptation under distribution shifts
Jian Liang, Ran He, and Tieniu Tan. 2023 · 2023
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Supervised fine-tuning and direct preference optimization on intel gaudi2
Kaokao Lv, Wenxin Zhang, Haihao Shen, and Intel Corporation. 2023 · 2023
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Orca 2: Teaching small language models how to reason
Arindam Mitra, Luciano Del Corro, Shweti Mahajan, Andrés Codas, Clarisse Simões, Sahaj Agrawal, Xuxi Chen, Anastasia Razdaibiedina, Erik Jones, Kriti Aggarwal, Hamid Palangi, Guoqing Zheng, Corby Rosset, Hamed Khanpour, and Ahmed Awadallah. 2023 · 2023
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Few-shot fine-tuning vs. in-context learning: A fair comparison and evaluation
Marius Mosbach, Tiago Pimentel, Shauli Ravfogel, Dietrich Klakow, and Yanai Elazar. 2023 · 2023
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Orca: Progressive learning from complex explanation traces of GPT-4
Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, and Ahmed Awadallah. 2023 · 2023
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OpenAI. 2023 · 2023
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Comblm: Adapting black-box language models through small fine-tuned models
Aitor Ormazabal, Mikel Artetxe, and Eneko Agirre. 2023 · 2023
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Detecting pretraining data from large language models
Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu, Terra Blevins, Danqi Chen, and Luke Zettlemoyer. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, and et. al. Peter Albert. 2023 · 2023
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Radadapt: Radiology report summarization via lightweight domain adaptation of large language models
Dave Van Veen, Cara Van Uden, Maayane Attias, Anuj Pareek, Christian Bluethgen, Malgorzata Polacin, Wah Chiu, Jean-Benoit Delbrouck, Juan Manuel Zambrano Chaves, Curtis P. Langlotz, Akshay Chaudhari, and John M. Pauly. 2023 · 2023
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Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning
Lucas Weber, Elia Bruni, and Dieuwke Hupkes. 2023 · 2023
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Multimodal prompt learning for product title generation with extremely limited labels
Bang Yang, Fenglin Liu, Zheng Li, Qingyu Yin, Chenyu You, Bing Yin, and Yuexian Zou. 2023a · 2023
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Out-of-distribution generalization in natural language processing: Past, present, and future
Linyi Yang, Yaoxian Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Jingming Zhuo, Lingqiao Liu, Jindong Wang, Jennifer Foster, and Yue Zhang. 2023b · 2023
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ALERT: Adapt language models to reasoning tasks
Ping Yu, Tianlu Wang, Olga Golovneva, Badr AlKhamissi, Siddharth Verma, Zhijing Jin, Gargi Ghosh, Mona Diab, and Asli Celikyilmaz. 2023 · 2023
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Revisiting out-of-distribution robustness in NLP: benchmark, analysis, and llms evaluations
Lifan Yuan, Yangyi Chen, Ganqu Cui, Hongcheng Gao, Fangyuan Zou, Xingyi Cheng, Heng Ji, Zhiyuan Liu, and Maosong Sun. 2023 · 2023
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Trueteacher: Learning factual consistency evaluation with large language models
Zorik Gekhman, Jonathan Herzig, Roee Aharoni, Chen Elkind, and Idan Szpektor. 2023a · 2070
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