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Structured Complex Task Decomposition (SCTD) is the problem of breaking down a complex real-world task (such as planning a wedding) into a directed acyclic graph over individual steps that contribute to achieving the task, with edges specifying temporal dependencies between them.
Language models are few-shot learners
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Language models as knowledge bases?
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The Hungarian method for the assignment problem
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Getting Things Done: The Art of Stress-Free Productivity
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Lin, B. Y.; Lee, S.; Khanna, R.; and Ren, X. 2020 · 2005
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Cascade: Crowdsourcing taxonomy creation
Chilton, L. B.; Little, G.; Edge, D.; Weld, D. S.; and Landay, J. A. 2013 · 2008
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Do language embeddings capture scales?
Zhang, X.; Ramachandran, D.; Tenney, I.; Elazar, Y.; and Roth, D. 2020 · 2010
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Taskgenies: Automatically providing action plans helps people complete tasks
Kokkalis, N.; Köhn, T.; Huebner, J.; Lee, M.; Schulze, F.; and Klemmer, S. R. 2013 · 2013
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Supporting complex search tasks
Awadallah, A. H.; White, R. W.; Pantel, P.; Dumais, S. T.; and Wang, Y.-M. 2014 · 2014
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Glove: Global vectors for word representation
Pennington, J.; Socher, R.; and Manning, C. D. 2014 · 2014
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Break it down: A comparison of macro-and microtasks
Cheng, J.; Teevan, J.; Iqbal, S. T.; and Bernstein, M. S. 2015 · 2015
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Task-based recommendation on a web-scale
Zhang, Y.; Zhang, M.; Liu, Y.; Tat-Seng, C.; Zhang, Y.; and Ma, S. 2015 · 2015
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Supporting collaborative writing with microtasks
Teevan, J.; Iqbal, S. T.; and Von Veh, C. 2016 · 2016
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Extracting hierarchies of search tasks & subtasks via a bayesian nonparametric approach
Mehrotra, R.; and Yilmaz, E. 2017 · 2017
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Cer, D.; Yang, Y.; Kong, S.-y.; Hua, N.; Limtiaco, N.; John, R. S.; Constant, N.; Guajardo-Cespedes, M.; Yuan, S.; Tar, C.; et al. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
proscript: Partially ordered scripts generation via pre-trained language models
Sakaguchi, K.; Bhagavatula, C.; Bras, R. L.; Tandon, N.; Clark, P.; and Choi, Y. 2021 · 2021
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Can Language Models be Biomedical Knowledge Bases?
Sung, M.; Lee, J.; Yi, S.; Jeon, M.; Kim, S.; and Kang, J. 2021 · 2021
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Symbolic knowledge distillation: from general language models to commonsense models
West, P.; Bhagavatula, C.; Hessel, J.; Hwang, J. D.; Jiang, L.; Bras, R. L.; Lu, X.; Welleck, S.; and Choi, Y. 2021 · 2021
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Learning to decompose and organize complex tasks
Zhang, Y.; Jauhar, S. K.; Kiseleva, J.; White, R.; and Roth, D. 2021 · 2021
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Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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Commonsense knowledge mining from pretrained models
Davison, J.; Feldman, J.; and Rush, A. M. 2019 · 2019
Cited alongside, same era.
How can we know what language models know?
Jiang, Z.; Xu, F. F.; Araki, J.; and Neubig, G. 2020 · 2020
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Evaluating commonsense in pre-trained language models
Zhou, X.; Zhang, Y.; Cui, L.; and Huang, D. 2020 · 2020
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The power of scale for parameter-efficient prompt tuning
Lester, B.; Al-Rfou, R.; and Constant, N. 2021 · 2021
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Show your work: Scratchpads for intermediate computation with language models
Nye, M.; Andreassen, A. J.; Gur-Ari, G.; Michalewski, H.; Austin, J.; Bieber, D.; Dohan, D.; Lewkowycz, A.; Bosma, M.; Luan, D.; et al. 2021 · 2021
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BoardgameQA: A Dataset for Natural Language Reasoning with Contradictory Information
Kazemi, M.; Yuan, Q.; Bhatia, D.; Kim, N.; Xu, X.; Imbrasaite, V.; and Ramachandran, D. 2023a
Cited in the paper.
Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H. W.; Sutton, C.; Gehrmann, S.; et al. 2022 · 2022
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Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Huang, W.; Abbeel, P.; Pathak, D.; and Mordatch, I. 2022 · 2022
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Language models of code are few-shot commonsense learners
Madaan, A.; Zhou, S.; Alon, U.; Yang, Y.; and Neubig, G. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Xia, F.; Chi, E.; Le, Q. V.; Zhou, D.; et al. 2022 · 2022
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GeoMLAMA: Geo-Diverse Commonsense Probing on Multilingual Pre-Trained Language Models
Yin, D.; Bansal, H.; Monajatipoor, M.; Li, L. H.; and Chang, K.-W. 2022 · 2022
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Brahman, F.; Bhagavatula, C.; Pyatkin, V.; Hwang, J. D.; Li, X. L.; Arai, H. J.; Sanyal, S.; Sakaguchi, K.; Ren, X.; and Choi, Y. 2023 · 2023
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Dr. ICL: Demonstration-Retrieved In-context Learning
Luo, M.; Xu, X.; Dai, Z.; Pasupat, P.; Kazemi, M.; Baral, C.; Imbrasaite, V.; and Zhao, V. Y. 2023 · 2023
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