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Large language models (LLMs) have been shown to be capable of impressive few-shot generalisation to new tasks.
Towards ai-complete question answering: A set of prerequisite toy tasks
J. Weston, A. Bordes, S. Chopra, A. M. Rush, B. Van Merriënboer, A. Joulin, and T. Mikolov · 2015
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Neural module networks
J. Andreas, M. Rohrbach, T. Darrell, and D. Klein · 2016
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Bigbench v2: The new and improved bigbench
A. Ghazal, T. Ivanov, P. Kostamaa, A. Crolotte, R. Voong, M. Al-Kateb, W. Ghazal, and R. V. Zicari · 2017
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Constructing datasets for multi-hop reading comprehension across documents
J. Welbl, P. Stenetorp, and S. Riedel · 2018
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Neural-symbolic vqa: Disentangling reasoning from vision and language understanding
K. Yi, J. Wu, C. Gan, A. Torralba, P. Kohli, and J. B. Tenenbaum · 2018
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Reconciling deep learning with symbolic artificial intelligence: representing objects and relations
M. Garnelo and M. Shanahan · 2019
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Neural module networks for reasoning over text
N. Gupta, K. Lin, D. Roth, S. Singh, and M. Gardner · 2019
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Learning by abstraction: The neural state machine
D. A. Hudson and C. D. Manning · 2019
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What ai can learn from romeo & juliet, Jul 2019
D. Lenat · 2019
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J. Mao, C. Gan, P. Kohli, J. B. Tenenbaum, and J. Wu · 2019
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Rebooting AI: Building Artificial Intelligence We Can Trust
G. Marcus and E. Davis · 2019
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200,000+ jeopardy! questions, Nov 2019
B. Tunguz · 2019
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Critical thinking for language models
G. Betz, C. Voigt, and K. Richardson · 2020
Cited alongside, same era.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
Cited alongside, same era.
Transformers as soft reasoners over language
P. Clark, O. Tafjord, and K. Richardson · 2020
Cited alongside, same era.
Neurosymbolic ai: the 3rd wave
A. d. Garcez and L. C. Lamb · 2020
Cited alongside, same era.
H. Jhamtani and P. Clark · 2020
Explaining answers with entailment trees
B. Dalvi, P. A. Jansen, O. Tafjord, Z. Xie, H. Smith, L. Pipatanangkura, and P. Clark · 2021
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Tell me why!–explanations support learning of relational and causal structure
A. K. Lampinen, N. A. Roy, I. Dasgupta, S. C. Chan, A. C. Tam, J. L. McClelland, C. Yan, A. Santoro, N. C. Rabinowitz, J. X. Wang, et al · 2021
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A systematic investigation of commonsense understanding in large language models
X. L. Li, A. Kuncoro, C. de Masson d’Autume, P. Blunsom, and A. Nematzadeh · 2021
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Pretrained transformers as universal computation engines
K. Lu, A. Grover, P. Abbeel, and I. Mordatch · 2021
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Webgpt: Browser-assisted question-answering with human feedback
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Cited alongside, same era.
B. Y. Lin, S. Lee, R. Khanna, and X. Ren · 2020
Cited alongside, same era.
The next decade in ai: four steps towards robust artificial intelligence
G. Marcus · 2020
Cited alongside, same era.
Prover: Proof generation for interpretable reasoning over rules
S. Saha, S. Ghosh, S. Srivastava, and M. Bansal · 2020
Cited alongside, same era.
Proofwriter: Generating implications, proofs, and abductive statements over natural language
O. Tafjord, B. D. Mishra, and P. Clark · 2020
Cited alongside, same era.
Deep learning for ai
Y. Bengio, Y. Lecun, and G. Hinton · 2021
Cited alongside, same era.
On the opportunities and risks of foundation models
R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill, et al · 2021
Cited alongside, same era.
Training verifiers to solve math word problems
K. Cobbe, V. Kosaraju, M. Bavarian, J. Hilton, R. Nakano, C. Hesse, and J. Schulman · 2021
Cited alongside, same era.
R. Nakano, J. Hilton, S. Balaji, J. Wu, L. Ouyang, C. Kim, C. Hesse, S. Jain, V. Kosaraju, W. Saunders, et al · 2021
Later among the works it cites.
Scaling language models: Methods, analysis & insights from training gopher
J. W. Rae, S. Borgeaud, T. Cai, K. Millican, J. Hoffmann, F. Song, J. Aslanides, S. Henderson, R. Ring, S. Young, et al · 2021
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Internet-augmented language models through few-shot prompting for open-domain question answering
A. Lazaridou, E. Gribovskaya, W. Stokowiec, and N. Grigorev · 2022
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Teaching language models to support answers with verified quotes
J. Menick, M. Trebacz, V. Mikulik, J. Aslanides, F. Song, M. Chadwick, M. Glaese, S. Young, L. Campbell-Gillingham, G. Irving, et al · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
S. Min, X. Lyu, A. Holtzman, M. Artetxe, M. Lewis, H. Hajishirzi, and L. Zettlemoyer · 2022
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Chain of thought prompting elicits reasoning in large language models, 2022
J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, and D. Zhou · 2022
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Star: Bootstrapping reasoning with reasoning
E. Zelikman, Y. Wu, and N. D. Goodman · 2022
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