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W4387316227 abstract "We present a large-scale empirical investigation of the zero-shot learning phenomena in a specific recognizing textual entailment (RTE) task category, i.e., the automated mining of LEADERBOARDS for Empirical AI Research. The prior reported state-of-the-art models for LEADERBOARDS extraction formulated as an RTE task in a non-zero-shot setting are promising with above 90% reported performances. However, a central research question remains unexamined: did the models actually learn entailment? Thus, for the experiments in this paper, two prior reported state-of-the-art models are tested out-of-the-box for their ability to generalize or their capacity for entailment, given LEADERBOARD labels that were unseen during training. We hypothesize that if the models learned entailment, their zero-shot performances can be expected to be moderately high as well-perhaps, concretely, better than chance. As a result of this work, a zero-shot labeled dataset is created via distant labeling, formulating the LEADERBOARD extraction RTE task." @default.
W4387316227 created "2023-10-04" @default.
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W4387316227 date "2023-06-01" @default.
W4387316227 modified "2023-10-06" @default.
W4387316227 title "Zero-Shot Entailment of Leaderboards for Empirical AI Research" @default.
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W4387316227 doi "https://doi.org/10.1109/jcdl57899.2023.00042" @default.
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