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Compositional generalization--understanding unseen combinations of seen primitives--is an essential reasoning capability in human intelligence.
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
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Syntactic structures (the hague: Mouton, 1957)
Noam Chomsky. 1957 · 1957
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English as a formal language
R Montague. 1974 · 1974
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Learning and development in neural networks: the importance of starting small
Jeffrey L. Elman. 1993 · 1993
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Learning to parse database queries using inductive logic programming
John M Zelle and Raymond J Mooney. 1996 · 1996
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The compositionality papers
Jerry A Fodor and Ernest Lepore. 2002 · 2002
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Learning to transform natural to formal languages
Rohit J Kate, Yuk Wah Wong, Raymond J Mooney, et al. 2005 · 2005
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Compositional generalization in semantic parsing: Pre-training vs. specialized architectures
Daniel Furrer, Marc van Zee, Nathan Scales, and Nathanael Schärli. 2020 · 2007
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Frequency of basic english grammatical structures: A corpus analysis
Douglas Roland, Frederic Dick, and Jeffrey L Elman. 2007 · 2007
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
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Luke S Zettlemoyer and Michael Collins. 2012 · 2012
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden Lake and Marco Baroni. 2018 · 2018
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Measuring compositional generalization: A comprehensive method on realistic data
Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, et al. 2019 · 2019
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Learning to recombine and resample data for compositional generalization
Ekin Akyürek, Afra Feyza Akyürek, and Jacob Andreas. 2020 · 2020
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Good-enough compositional data augmentation
Jacob Andreas. 2020 · 2020
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Compositional generalization via neural-symbolic stack machines
Xinyun Chen, Chen Liang, Adams Wei Yu, Dawn Song, and Denny Zhou. 2020 · 2020
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Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020 · 2020
Cited alongside, same era.
Cogs: A compositional generalization challenge based on semantic interpretation
Najoung Kim and Tal Linzen. 2020 · 2020
Cited alongside, same era.
Compositional generalization by learning analytical expressions
Qian Liu, Shengnan An, Jian-Guang Lou, Bei Chen, Zeqi Lin, Yan Gao, Bin Zhou, Nanning Zheng, and Dongmei Zhang. 2020 · 2020
Cited alongside, same era.
The devil is in the detail: Simple tricks improve systematic generalization of transformers
Róbert Csordás, Kazuki Irie, and Juergen Schmidhuber. 2021 · 2021
Cited alongside, same era.
Revisiting iterative back-translation from the perspective of compositional generalization
Yinuo Guo, Hualei Zhu, Zeqi Lin, Bei Chen, Jian-Guang Lou, and Dongmei Zhang. 2021 · 2021
Cited alongside, same era.
Learning to generalize compositionally by transferring across semantic parsing tasks
Wang Zhu, Peter Shaw, Tal Linzen, and Fei Sha. 2021 · 2021
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Unobserved local structures make compositional generalization hard
Ben Bogin, Shivanshu Gupta, and Jonathan Berant. 2022 · 2022
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Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al. 2022 · 2022
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Compositional semantic parsing with large language models
Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, and Denny Zhou. 2022 · 2022
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Jonathan Herzig and Jonathan Berant. 2021 · 2021
Cited alongside, same era.
Unlocking compositional generalization in pre-trained models using intermediate representations
Jonathan Herzig, Peter Shaw, Ming-Wei Chang, Kelvin Guu, Panupong Pasupat, and Yuan Zhang. 2021 · 2021
Cited alongside, same era.
Learning algebraic recombination for compositional generalization
Chenyao Liu, Shengnan An, Zeqi Lin, Qian Liu, Bei Chen, Jian-Guang Lou, Lijie Wen, Nanning Zheng, and Dongmei Zhang. 2021 · 2021
Cited alongside, same era.
Finding needles in a haystack: Sampling structurally-diverse training sets from synthetic data for compositional generalization
Inbar Oren, Jonathan Herzig, and Jonathan Berant. 2021 · 2021
Cited alongside, same era.
Mapping language models to grounded conceptual spaces
Roma Patel and Ellie Pavlick. 2021 · 2021
Cited alongside, same era.
Synchromesh: Reliable code generation from pre-trained language models
Gabriel Poesia, Alex Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani. 2021 · 2021
Cited alongside, same era.
Scaling language models: Methods, analysis & insights from training gopher
Jack W Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, et al. 2021 · 2021
Cited alongside, same era.
Training compute-optimal large language models
Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, et al. 2022 · 2022
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On the compositional generalization gap of in-context learning
Arian Hosseini, Ankit Vani, Dzmitry Bahdanau, Alessandro Sordoni, and Aaron Courville. 2022 · 2022
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Najoung Kim, Tal Linzen, and Paul Smolensky. 2022 · 2022
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Diverse demonstrations improve in-context compositional generalization
Itay Levy, Ben Bogin, and Jonathan Berant. 2022 · 2022
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What makes good in-context examples for gpt-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, William B Dolan, Lawrence Carin, and Weizhu Chen. 2022 · 2022
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Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022 · 2022
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Language models of code are few-shot commonsense learners
Aman Madaan, Shuyan Zhou, Uri Alon, Yiming Yang, and Graham Neubig. 2022 · 2022
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Evaluating the impact of model scale for compositional generalization in semantic parsing
Linlu Qiu, Peter Shaw, Panupong Pasupat, Tianze Shi, Jonathan Herzig, Emily Pitler, Fei Sha, and Kristina Toutanova. 2022 · 2022
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Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, et al. 2022 · 2022
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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. 2022 · 2022
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al. 2022 · 2022
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Does deep learning learn to abstract? a systematic probing framework
Shengnan An, Zeqi Lin, Bei Chen, Qiang Fu, Nanning Zheng, and Jian-Guang Lou. 2023 · 2023
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