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Compositional learning, mastering the ability to combine basic concepts and construct more intricate ones, is crucial for human cognition, especially in human language comprehension and visual perception.
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A benchmark for systematic generalization in grounded language understanding, 2020
Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, and Brenden M. Lake · 2003
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Compositionality in Formal Semantics: Selected Papers of Barbara H. Partee
Barbara Hall Partee · 2004
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Jon Kleinberg and Eva Tardos · 2005
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Obtaining faithful interpretations from compositional neural networks, 2020
Sanjay Subramanian, Ben Bogin, Nitish Gupta, Tomer Wolfson, Sameer Singh, Jonathan Berant, and Matt Gardner · 2005
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Dog is a dog is a dog: Infant rule learning is not specific to language
Jenny Saffran, Seth Pollak, Rebecca Seibel, and Anna Shkolnik · 2006
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The Harmonic Mind: From Neural Computation to Optimality-Theoretic GrammarVolume I: Cognitive Architecture (Bradford Books)
Paul Smolensky and Géraldine Legendre · 2006
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Compositional networks enable systematic generalization for grounded language understanding, 2020
Yen-Ling Kuo, Boris Katz, and Andrei Barbu · 2008
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Object detection with grammar models
Ross Girshick, Pedro Felzenszwalb, and David McAllester · 2011
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Conceptual and procedural knowledge: The case of mathematics
J. Hiebert · 2013
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Deep recursive neural networks for compositionality in language
Ozan Irsoy and Claire Cardie · 2014
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A SICK cure for the evaluation of compositional distributional semantic models
Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, and Roberto Zamparelli · 2014
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Yelp dataset, 2014
Yelp · 2014
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Fine-grained visual comparisons with local learning
Aron Yu and Kristen Grauman · 2014
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Tree-structured composition in neural networks without tree-structured architectures
Samuel R. Bowman, Christopher D. Manning, and Christopher Potts · 2015
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Deep residual learning for image recognition
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2015
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Discovering states and transformations in image collections
Phillip Isola, Joseph J. Lim, and Edward H. Adelson · 2015
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Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein · 2016
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
Ruining He and Julian McAuley · 2016
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Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, and Ross B. Girshick · 2016
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Convolutional sequence to sequence learning
Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, and Yann N. Dauphin · 2017
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden M. Lake and Marco Baroni · 2017
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Building machines that learn and think like people
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom · 2017
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Semantic jitter: Dense supervision for visual comparisons via synthetic images
Aron Yu and Kristen Grauman · 2017
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Measuring abstract reasoning in neural networks
David Barrett, Felix Hill, Adam Santoro, Ari Morcos, and Timothy Lillicrap · 2018
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Compositional Reasoning , pp. 345–383
Dimitra Giannakopoulou, Kedar S. Namjoshi, and Corina S. Păsăreanu · 2018
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Visualisation and ‘diagnostic classifiers’ reveal how recurrent and recursive neural networks process hierarchical structure
Dieuwke Hupkes, Sara Veldhoen, and Willem Zuidema · 2018
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Deep learning: A critical appraisal, 2018
Gary Marcus · 2018
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A roadmap towards machine intelligence
Tomas Mikolov, Armand Joulin, and Marco Baroni · 2018
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Houdini: lifelong learning as program synthesis
Lazar Valkov, Dipak Chaudhari, Akash Srivastava, Charles Sutton, and Swarat Chaudhuri · 2018
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Systematic generalization: What is required and can it be learned?
Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, and Aaron Courville · 2019
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Universal transformers
Mostafa Dehghani, Stephan Gouws, Oriol Vinyals, Jakob Uszkoreit, and Lukasz Kaiser · 2019
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CNNs found to jump around more skillfully than RNNs: Compositional generalization in seq2seq convolutional networks
Roberto Dessì and Marco Baroni · 2019
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It’s not about the journey; it’s about the destination: Following soft paths under question-guidance for visual reasoning
Monica Haurilet, Alina Roitberg, and Rainer Stiefelhagen · 2019
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Transcoding compositionally: Using attention to find more generalizable solutions
Kris Korrel, Dieuwke Hupkes, Verna Dankers, and Elia Bruni · 2019
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Human few-shot learning of compositional instructions
Brenden M. Lake, Tal Linzen, and Marco Baroni · 2019
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Multiple-attribute text rewriting
Guillaume Lample, Sandeep Subramanian, Eric Smith, Ludovic Denoyer, Marc’Aurelio Ranzato, and Y-Lan Boureau · 2019
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On the turing completeness of modern neural network architectures
Jorge Pérez, Javier Marinković, and Pablo Barceló · 2019
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CLUTRR: A diagnostic benchmark for inductive reasoning from text
Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William L. Hamilton · 2019
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Learning compositional representations for few-shot recognition
Pavel Tokmakov, Yu-Xiong Wang, and Martial Hebert · 2019
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Closure: Assessing systematic generalization of clevr models, 2020
Dzmitry Bahdanau, Harm de Vries, Timothy J. O’Donnell, Shikhar Murty, Philippe Beaudoin, Yoshua Bengio, and Aaron Courville · 2020
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, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus · 2022
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Disentangled sequence to sequence learning for compositional generalization
Hao Zheng and Mirella Lapata · 2022
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Learning threshold neurons via edge of stability
Kwangjun Ahn, Sebastien Bubeck, Sinho Chewi, Yin Tat Lee, Felipe Suarez, and Yi Zhang · 2023
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A theory for emergence of complex skills in language models, 2023
Sanjeev Arora and Anirudh Goyal · 2023
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Transformer working memory enables regular language reasoning and natural language length extrapolation
Ta-Chung Chi, Ting-Han Fan, Alexander Rudnicky, and Peter Ramadge · 2023
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Climbing towards NLU: On meaning, form, and understanding in the age of data
Emily M. Bender and Alexander Koller · 2020
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Systematic generalization on gSCAN with language conditioned embedding
Tong Gao, Qi Huang, and Raymond Mooney · 2020
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann · 2020
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Measuring systematic generalization in neural proof generation with transformers
Nicolas Gontier, Koustuv Sinha, Siva Reddy, and Chris Pal · 2020
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Theoretical limitations of self-attention in neural sequence models
Michael Hahn · 2020
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RNNs can generate bounded hierarchical languages with optimal memory
John Hewitt, Michael Hahn, Surya Ganguli, Percy Liang, and Christopher D. Manning · 2020
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Faith and fate: Limits of transformers on compositionality
Nouha Dziri, Ximing Lu, Melanie Sclar, Xiang (Lorraine) Li, Liwei Jiang, Bill Yuchen Lin, Sean Welleck, Peter West, Chandra Bhagavatula, Ronan Le Bras, Jena Hwang, Soumya Sanyal, Xiang Ren, Allyson Ettinger, Zaid Harchaoui, and Yejin Choi · 2023
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Towards revealing the mystery behind chain of thought: A theoretical perspective
Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye, Di He, and Liwei Wang · 2023
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Training vision-language transformers from captions
Liangke Gui, Yingshan Chang, Qiuyuan Huang, Subhojit Som, Alexander G Hauptmann, Jianfeng Gao, and Yonatan Bisk · 2023
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Visual programming: Compositional visual reasoning without training
Tanmay Gupta and Aniruddha Kembhavi · 2023
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What’s left? concept grounding with logic-enhanced foundation models
Joy Hsu, Jiayuan Mao, Joshua B. Tenenbaum, and Jiajun Wu · 2023
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Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, and Ting Liu · 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 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
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Cladder: Assessing causal reasoning in language models
Zhijing Jin*, Yuen Chen*, Felix Leeb*, Luigi Gresele*, Ojasv Kamal, Zhiheng Lyu, Kevin Blin, Fernando Gonzalez, Max Kleiman-Weiner, Mrinmaya Sachan, and Bernhard Schölkopf · 2023
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Syntax-guided transformers: Elevating compositional generalization and grounding in multimodal environments
Danial Kamali and Parisa Kordjamshidi · 2023
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The impact of positional encoding on length generalization in transformers
Amirhossein Kazemnejad, Inkit Padhi, Karthikeyan Natesan, Payel Das, and Siva Reddy · 2023
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Decomposed prompting: A modular approach for solving complex tasks
Tushar Khot, Harsh Trivedi, Matthew Finlayson, Yao Fu, Kyle Richardson, Peter Clark, and Ashish Sabharwal · 2023
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A joint study of phrase grounding and task performance in vision and language models
Noriyuki Kojima, Hadar Averbuch-Elor, and Yoav Artzi · 2023
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Break it down: Evidence for structural compositionality in neural networks
Michael A. Lepori, Thomas Serre, and Ellie Pavlick · 2023
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Does continual learning meet compositionality? new benchmarks and an evaluation framework
Weiduo Liao, Ying Wei, Mingchen Jiang, Qingfu Zhang, and Hisao Ishibuchi · 2023
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Jorge A. Mendez and Eric Eaton · 2023
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The parallelism tradeoff: Limitations of log-precision transformers
William Merrill and Ashish Sabharwal · 2023
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Pushdown layers: Encoding recursive structure in transformer language models
Shikhar Murty, Pratyusha Sharma, Jacob Andreas, and Christopher Manning · 2023
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Compositional abilities emerge multiplicatively: Exploring diffusion models on a synthetic task
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Certified reasoning with language models, 2023
Gabriel Poesia, Kanishk Gandhi, Eric Zelikman, and Noah D. Goodman · 2023
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Measuring and narrowing the compositionality gap in language models
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A. Smith, and Mike Lewis · 2023
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Reasoning with language model prompting: A survey
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How compositional is a model?
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Randomized positional encodings boost length generalization of transformers
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NLP evaluation in trouble: On the need to measure LLM data contamination for each benchmark
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MetaReVision: Meta-learning with retrieval for visually grounded compositional concept acquisition
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