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The Abstraction and Reasoning Corpus (ARC) is a set of procedural tasks that tests an agent's ability to flexibly solve novel problems.
Remembering: A study in experimental and social psychology
Frederic Charles Bartlett · 1932
Earlier work this paper cites.
The formation of learning sets
Harry F Harlow · 1949
Earlier work this paper cites.
Common ground at the understanding of demonstrative reference
Herbert H Clark, Robert Schreuder, and Samuel Buttrick · 1983
Earlier work this paper cites.
An iterative design methodology for user-friendly natural language office information applications
John F Kelley · 1984
Earlier work this paper cites.
Referring as a collaborative process
Herbert H Clark and Deanna Wilkes-Gibbs · 1986
Earlier work this paper cites.
The estimation of stochastic context-free grammars using the inside-outside algorithm
Karim Lari and Steve J Young · 1990
Earlier work this paper cites.
Origins of knowledge
Elizabeth S Spelke, Karen Breinlinger, Janet Macomber, and Kristen Jacobson · 1992
Earlier work this paper cites.
A study on prosody and discourse structure in cooperative dialogues
Shin’ya Nakajima and James F Allen · 1993
Earlier work this paper cites.
The structure and function of explanations
Tania Lombrozo · 2006
Earlier work this paper cites.
Combinatorial sketching for finite programs
Armando Solar-Lezama, Liviu Tancau, Rastislav Bodik, Sanjit Seshia, and Vijay Saraswat · 2006
Earlier work this paper cites.
Core knowledge
Elizabeth S. Spelke and Katherine D. Kinzler · 2007
Earlier work this paper cites.
Program Synthesis By Sketching
Armando Solar Lezama · 2008
Earlier work this paper cites.
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Yizao Wang, Jean-Yves Audibert, and Rémi Munos · 2008
Earlier work this paper cites.
Weakly supervised learning of semantic parsers for mapping instructions to actions
Yoav Artzi and Luke Zettlemoyer · 2013
Earlier work this paper cites.
Bootstrap learning via modular concept discovery
Eyal Dechter, Jon Malmaud, Ryan P Adams, and Joshua B Tenenbaum · 2013
Earlier work this paper cites.
The nature of expertise
Michelene TH Chi, Robert Glaser, and Marshall J Farr · 2014
Earlier work this paper cites.
Learning compact lexicons for ccg semantic parsing
Yoav Artzi, Dipanjan Das, and Slav Petrov · 2014
Earlier work this paper cites.
Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
Earlier work this paper cites.
Building a semantic parser overnight
Yushi Wang, Jonathan Berant, and Percy Liang · 2015
Earlier work this paper cites.
Inductive programming meets the real world
Sumit Gulwani, José Hernández-Orallo, Emanuel Kitzelmann, Stephen H Muggleton, Ute Schmid, and Benjamin Zorn · 2015
Earlier work this paper cites.
Neuro-symbolic program synthesis
Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, and Pushmeet Kohli · 2016
Earlier work this paper cites.
Learning language games through interaction
Sida I Wang, Percy Liang, and Christopher D Manning · 2016
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Learning executable semantic parsers for natural language understanding
Percy Liang · 2016
Cited alongside, same era.
A network-based end-to-end trainable task-oriented dialogue system
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Cited alongside, same era.
Data recombination for neural semantic parsing
Robin Jia and Percy Liang · 2016
Cited alongside, same era.
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Unnatural language processing: Bridging the gap between synthetic and natural language data
Alana Marzoev, Samuel Madden, M Frans Kaashoek, Michael Cafarella, and Jacob Andreas · 2020
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Program synthesis with pragmatic communication
Yewen Pu, Kevin Ellis, Marta Kryven, Josh Tenenbaum, and Armando Solar-Lezama · 2020
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Jshrink: in-depth investigation into debloating modern java applications
Bobby R Bruce, Tianyi Zhang, Jaspreet Arora, Guoqing Harry Xu, and Miryung Kim · 2020
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Dreamcoder: Growing generalizable, interpretable knowledge with wake-sleep bayesian program learning
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, and Joshua B Tenenbaum · 2020
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Does your code need comment?
Yuan Huang, Nan Jia, Junhuai Shu, Xinyu Hu, Xiangping Chen, and Qiang Zhou · 2020
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2017
Cited alongside, same era.
Building machines that learn and think like people
Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman · 2017
Cited alongside, same era.
Robustfill: Neural program learning under noisy i/o
Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, and Pushmeet Kohli · 2017
Cited alongside, same era.
From language to programs: Bridging reinforcement learning and maximum marginal likelihood
Kelvin Guu, Panupong Pasupat, Evan Zheran Liu, and Percy Liang · 2017
Cited alongside, same era.
Multiwoz–a large-scale multi-domain wizard-of-oz dataset for task-oriented dialogue modelling
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Task-oriented dialogue as dataflow synthesis
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Later among the works it cites.
Learning rewards from linguistic feedback
Theodore R Sumers, Mark K Ho, Robert D Hawkins, Karthik Narasimhan, and Thomas L Griffiths · 2020
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Fast and flexible: Human program induction in abstract reasoning tasks
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Constrained language models yield few-shot semantic parsers
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Learning transferable visual models from natural language supervision
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Finetuned language models are zero-shot learners
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Craft an iron sword: Dynamically generating interactive game characters by prompting large language models tuned on code
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