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Large language models (LLMs) can learn to perform a wide range of natural language tasks from just a handful of in-context examples.
An efficient context-free parsing algorithm
Jay Earley · 1970
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STRIPS: A new approach to the application of theorem proving to problem solving
Richard E Fikes and Nils J Nilsson · 1971
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Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
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Learning to parse database queries using inductive logic programming
John M Zelle and Raymond J Mooney · 1996
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PDDL – the planning domain definition language
Malik Ghallab, Adele Howe, Craig Knoblock, Drew McDermott, Ashwin Ram, Manuela Veloso, Daniel Weld, David Wilkins SRI, Anthony Barrett, Dave Christianson, et al · 1998
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PDDL2. 1: An extension to PDDL for expressing temporal planning domains
Maria Fox and Derek Long · 2003
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The metric-ff planning system: Translating“ignoring delete lists”to numeric state variables
Jörg Hoffmann · 2003
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Macro-ff: Improving AI planning with automatically learned macro-operators
Adi Botea, Markus Enzenberger, Martin Müller, and Jonathan Schaeffer · 2005
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Modelling mixed discrete-continuous domains for planning
Maria Fox and Derek Long · 2006
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Quasi-Synchronous Grammars: Alignment by Soft Projection of Syntactic Dependencies
David Smith and Jason Eisner · 2006
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What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA
Mengqiu Wang, Noah A. Smith, and Teruko Mitamura · 2007
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Building a semantic parser overnight
Yushi Wang, Jonathan Berant, and Percy Liang · 2015
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Recurrent neural network grammars
Chris Dyer, Adhiguna Kuncoro, Miguel Ballesteros, and Noah A. Smith · 2016
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Unsupervised neural dependency parsing
Yong Jiang, Wenjuan Han, Kewei Tu, et al · 2016
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Program synthesis
Sumit Gulwani, Oleksandr Polozov, Rishabh Singh, et al · 2017
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Focused model-learning and planning for non-Gaussian continuous state-action systems
Zi Wang, Stefanie Jegelka, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2017
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Learning STRIPS action models with classical planning
Diego Aineto, Sergio Jiménez, and Eva Onaindia · 2018
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Syntax-directed variational autoencoder for structured data
Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, and Le Song · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Aishwarya Kamath and Rajarshi Das · 2018
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Active model learning and diverse action sampling for task and motion planning
Zi Wang, Caelan Reed Garrett, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2018
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Semantic parsing with dual learning
Ruisheng Cao, Su Zhu, Chen Liu, Jieyu Li, and Kai Yu · 2019
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Unsupervised learning of PCFGs with normalizing flow
Lifeng Jin, Finale Doshi-Velez, Timothy Miller, Lane Schwartz, and William Schuler · 2019
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Molecular hypergraph grammar with its application to molecular optimization
Hiroshi Kajino · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych · 2019
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Smiles-bert: large scale unsupervised pre-training for molecular property prediction
Sheng Wang, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang · 2019
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Learning sparse relational transition models
Victoria Xia, Zi Wang, and Leslie Pack Kaelbling · 2019
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Pyperplan (v1.3), 2020
Yusra Alkhazraji, Matthias Frorath, Markus Grützner, Malte Helmert, Thomas Liebetraut, Robert Mattmüller, Manuela Ortlieb, Jendrik Seipp, Tobias Springenberg, Philip Stahl, and Jan Wülfing · 2020
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Task-oriented dialogue as dataflow synthesis
Jacob Andreas, John Bufe, David Burkett, Charles Chen, Josh Clausman, Jean Crawford, Kate Crim, Jordan DeLoach, Leah Dorner, Jason Eisner, Hao Fang, Alan Guo, David Hall, Kristin Hayes, Kellie Hill, Diana Ho, Wendy Iwaszuk, Smriti Jha, Dan Klein, Jayant Krishnamurthy, Theo Lanman, Percy Liang, Christopher H. Lin, Ilya Lintsbakh, Andy McGovern, Aleksandr Nisnevich, Adam Pauls, Dmitrij Petters, Brent Read, Dan Roth, Subhro Roy, Jesse Rusak, Beth Short, Div Slomin, Ben Snyder, Stephon Striplin, Yu Su, Zachary Tellman, Sam Thomson, Andrei Vorobev, Izabela Witoszko, Jason Wolfe, Abby Wray, Yuchen Zhang, and Alexander Zotov · 2020
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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, et al · 2020
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Retro*: Learning retrosynthetic planning with neural guided A* search
Binghong Chen, Chengtao Li, Hanjun Dai, and Le Song · 2020
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PDDLStream: Integrating symbolic planners and blackbox samplers
Data-efficient graph grammar learning for molecular generation
Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, and Wojciech Matusik · 2022
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Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch · 2022
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Diverse demonstrations improve in-context compositional generalization
Itay Levy, Ben Bogin, and Jonathan Berant · 2022
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On grounded planning for embodied tasks with language models
Bill Yuchen Lin, Chengsong Huang, Qian Liu, Wenda Gu, Sam Sommerer, and Xiang Ren · 2022
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Synchromesh: Reliable Code Generation from Pre-trained Language Models
Gabriel Poesia, Alex Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani · 2022
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Caelan R. Garrett, Tomas Lozano-Perez, and Leslie P. Kaelbling · 2020
Cited alongside, same era.
Self-referencing embedded strings (selfies): A 100% robust molecular string representation
Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, and Alan Aspuru-Guzik · 2020
Cited alongside, same era.
Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang · 2020
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Benchmarking multimodal regex synthesis with complex structures
Xi Ye, Qiaochu Chen, Isil Dillig, and Greg Durrett · 2020
Cited alongside, same era.
The return of lexical dependencies: Neural lexicalized PCFGs
Hao Zhu, Yonatan Bisk, and Graham Neubig · 2020
Cited alongside, same era.
Program synthesis with large language models
Jacob Austin, Augustus Odena, Maxwell Nye, Maarten Bosma, Henryk Michalewski, David Dohan, Ellen Jiang, Carrie Cai, Michael Terry, Quoc Le, et al · 2021
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba · 2021
Cited alongside, same era.
Span-based semantic parsing for compositional generalization
Jonathan Herzig and Jonathan Berant · 2021
Cited alongside, same era.
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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
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Planning with large language models via corrective re-prompting
Shreyas Sundara Raman, Vanya Cohen, Eric Rosen, Ifrah Idrees, David Paulius, and Stefanie Tellex · 2022
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, et al · 2022
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Few-shot semantic parsing with language models trained on code
Richard Shin and Benjamin Van Durme · 2022
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PDDL planning with pretrained large language models
Tom Silver, Varun Hariprasad, Reece S Shuttleworth, Nishanth Kumar, Tomás Lozano-Pérez, and Leslie Pack Kaelbling · 2022
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Neurosymbolic programming for science
Jennifer J Sun, Megan Tjandrasuwita, Atharva Sehgal, Armando Solar-Lezama, Swarat Chaudhuri, Yisong Yue, and Omar Costilla-Reyes · 2022
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Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Scharli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, and Jason Wei · 2022
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Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)
Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati · 2022
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Hierarchical Phrase-based Sequence-to-Sequence Learning
Bailin Wang, Ivan Titov, Jacob Andreas, and Yoon Kim · 2022
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A systematic evaluation of large language models of code
Frank F Xu, Uri Alon, Graham Neubig, and Vincent Josua Hellendoorn · 2022
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Rohan Anil, Andrew M Dai, Orhan Firat, Melvin Johnson, Dmitry Lepikhin, Alexandre Passos, Siamak Shakeri, Emanuel Taropa, Paige Bailey, Zhifeng Chen, et al · 2023
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Flexible Grammar-Based Constrained Decoding for Language Models
Saibo Geng, Martin Josifosky, Maxime Peyrard, and Robert West · 2023
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Solving math word problems by combining language models with symbolic solvers
Joy He-Yueya, Gabriel Poesia, Rose E Wang, and Noah D Goodman · 2023
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Chain-of-symbol prompting elicits planning in large langauge models
Hanxu Hu, Hongyuan Lu, Huajian Zhang, Wai Lam, and Yue Zhang · 2023
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TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs
Yaobo Liang, Chenfei Wu, Ting Song, Wenshan Wu, Yan Xia, Yu Liu, Yang Ou, Shuai Lu, Lei Ji, Shaoguang Mao, Yun Wang, Linjun Shou, Ming Gong, and Nan Duan · 2023
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Llm+ p: Empowering large language models with optimal planning proficiency
Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone · 2023
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Faithful chain-of-thought reasoning
Qing Lyu, Shreya Havaldar, Adam Stein, Li Zhang, Delip Rao, Eric Wong, Marianna Apidianaki, and Chris Callison-Burch · 2023
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Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning
Liangming Pan, Alon Albalak, Xinyi Wang, and William Yang Wang · 2023
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ART: Automatic multi-step reasoning and tool-use for large language models
Bhargavi Paranjape, Scott Lundberg anbd Sameer Singh, Hannaneh Hajishirzi, Luke Zettlemoyer, and Marco Tulio Ribeiro · 2023
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Tool learning with foundation models
Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, et al · 2023
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Toolformer: Language Models Can Teach Themselves to Use Tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
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ViperGPT: Visual Inference via Python Execution for Reasoning
Didac Suris, Sachit Menon, and Carl Vondrick · 2023
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Experimental results from applying GPT-4 to an unpublished formal language
Gregor vom Scheidt · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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