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Zero-shot reasoning methods with Large Language Models (LLMs) offer significant advantages including great generalization to novel tasks and reduced dependency on human-crafted examples.
Human problem solving: The state of the theory in 1970
Herbert A Simon and Allen Newell · 1971
Earlier work this paper cites.
Categorization and representation of physics problems by experts and novices
Michelene TH Chi, Paul J Feltovich, and Robert Glaser · 1981
Earlier work this paper cites.
Why are some problems hard? evidence from tower of hanoi
Kenneth Kotovsky, John R Hayes, and Herbert A Simon · 1985
Earlier work this paper cites.
Metaphors we live by
George Lakoff and Mark Johnson · 2008
Earlier work this paper cites.
Fever: a large-scale dataset for fact extraction and verification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal · 2018
Earlier work this paper cites.
Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning · 2018
Earlier work this paper cites.
Measuring massive multitask language understanding
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2020
Earlier work this paper cites.
An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma · 2021
Earlier work this paper cites.
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Andrew Drozdov, Nathanael Schärli, Ekin Akyürek, Nathan Scales, Xinying Song, Xinyun Chen, Olivier Bousquet, and Denny Zhou · 2022
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Cited alongside, same era.
Freshllms: Refreshing large language models with search engine augmentation
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Later among the works it cites.
URL https://huggingface.co/spaces/evaluate-metric/exact_match
Metric: exact_match, 2023 · 2024
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