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We propose a novel application of prompting Pre-trained Language Models (PLMs) to generate analogies and study how to design effective prompts for two task settings: generating a source concept analogous to a given target concept (aka Analogous Concept Generation or ACG), and generating an explanation of the similarity between a given pair of target concept and source concept (aka Analogous Explanation Generation or AEG).
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Danish Pruthi, Bhuwan Dhingra, and Zachary C Lipton. 2019 · 1905
Earlier work this paper cites.
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Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019 · 1910
Earlier work this paper cites.
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Maurice G Kendall. 1938 · 1938
Earlier work this paper cites.
Measuring nominal scale agreement among many raters
Joseph L Fleiss. 1971 · 1971
Earlier work this paper cites.
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Timothy J Newby, Peggy A Ertmer, and Donald A Stepich. 1995 · 1995
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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