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We systematically study how three large language models with code capabilities - CodeT5, Codex, and ChatGPT - generalize to out-of-domain data.
Modular universal reparameterization: Deep multi-task learning across diverse domains
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Algorithms and applications for multitask learning
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The parallel transfer of task knowledge using dynamic learning rates based on a measure of relatedness
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Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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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, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff 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 · 2005
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A study of mutual information based feature selection for case based reasoning in software cost estimation
Yan-Fu Li, Min Xie, and T. N. Goh. 2009 · 2009
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Codebleu: a method for automatic evaluation of code synthesis
Shuo Ren, Daya Guo, Shuai Lu, Long Zhou, Shujie Liu, Duyu Tang, M. Zhou, Ambrosio Blanco, and Shuai Ma. 2020 · 2009
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Cross-project defect prediction: a large scale experiment on data vs. domain vs. process
Thomas Zimmermann, Nachiappan Nagappan, Harald C. Gall, Emanuel Giger, and Brendan Murphy. 2009 · 2009
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Transfer learning for cross-company software defect prediction
Ying Ma, Guangchun Luo, Xue Zeng, and Aiguo Chen. 2012 · 2012
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On the dataset shift problem in software engineering prediction models
Burak Turhan. 2012 · 2012
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Siamese neural networks for one-shot image recognition
Gregory R. Koch. 2015 · 2015
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chrf: character n-gram f-score for automatic MT evaluation
Maja Popovic. 2015 · 2015
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Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew M. Botvinick, Daan Wierstra, and Timothy P. Lillicrap. 2016 · 2016
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Deep multi-task representation learning: A tensor factorisation approach
Yongxin Yang and Timothy M. Hospedales. 2016 · 2016
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Deep reinforcement learning from human preferences
Paul Francis Christiano, Jan Leike, Tom B. Brown, Miljan Martic, Shane Legg, and Dario Amodei. 2017 · 2017
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Estimating predictive uncertainty under program data distribution shift
Yufei Li, Simin Chen, and Wei Yang. 2021 · 2021
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What makes good in-context examples for gpt-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen. 2021 · 2021
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Constrained language models yield few-shot semantic parsers
Richard Shin, C. H. Lin, Sam Thomson, Charles C. Chen, Subhro Roy, Emmanouil Antonios Platanios, Adam Pauls, Dan Klein, Jas’ Eisner, and Benjamin Van Durme. 2021 · 2021
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, P. Abbeel, and Sergey Levine. 2017 · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017 · 2017
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Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Koray Kavukcuoglu, and Daan Wierstra. 2017 · 2017
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Antreas Antoniou, Harrison Edwards, and Amos J. Storkey. 2018 · 2018
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Probabilistic model-agnostic meta-learning
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Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, and Timothy M. Hospedales. 2017 · 2018
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Simple, scalable adaptation for neural machine translation
Ankur Bapna, N. Arivazhagan, and Orhan Firat. 2019 · 2019
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Yue Wang, Weishi Wang, Shafiq R. Joty, and Steven C. H. Hoi. 2021 · 2021
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Robust machine learning for malware detection over time
Daniele Angioni, Luca Demetrio, Maura Pintor, and Battista Biggio. 2022 · 2022
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Out of the BLEU: how should we assess quality of the code generation models?
Mikhail Evtikhiev, Egor Bogomolov, Yaroslav Sokolov, and Timofey Bryksin. 2022 · 2022
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Codes: A distribution shift benchmark dataset for source code learning
Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, and Yves Le Traon. 2022 · 2022
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Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven C. H. Hoi. 2022 · 2022
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Impact of evaluation methodologies on code summarization
Pengyu Nie, Jiyang Zhang, Junyi Jessy Li, Raymond J. Mooney, and Milos Gligoric. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke E. Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Francis Christiano, Jan Leike, and Ryan J. Lowe. 2022 · 2022
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Isoscore: Measuring the uniformity of embedding space utilization
William Rudman, Nate Gillman, Taylor Rayne, and Carsten Eickhoff. 2022 · 2022
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Selective annotation makes language models better few-shot learners
Hongjin Su, Jungo Kasai, Chen Henry Wu, Weijia Shi, Tianlu Wang, Jiayi Xin, Rui Zhang, Mari Ostendorf, Luke Zettlemoyer, Noah A. Smith, and Tao Yu. 2022 · 2022
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The vault: A comprehensive multilingual dataset for advancing code understanding and generation
Dung Nguyen Manh, Nam Le Hai, Anh T. V. Dau, Anh Minh Nguyen, Khanh Nghiem, Jin Guo, and Nghi D. Q. Bui. 2023 · 2023
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Codebertscore: Evaluating code generation with pretrained models of code
Shuyan Zhou, Uri Alon, Sumit Agarwal, and Graham Neubig. 2023 · 2023
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