@inproceedings{harvill-etal-2023-one-shot,
title = "One-Shot and Few-Shot Exemplification Modeling",
author = "Harvill, John and
Yoon, Hee Suk and
Yoon, Eunseop and
H a s e g a w a - J o h n s o n, Mark and
Yoo, Chang",
editor = "Gehrmann, Sebastian and
Wang, Alex and
Sedoc, Jo{\~a}o and
Clark, Elizabeth and
Dhole, Kaustubh and
Chandu, Khyathi Raghavi and
Santus, Enrico and
Sedghamiz, Hooman",
booktitle = "Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.gem-1.7",
pages = "76--87",
abstract = "Exemplification modeling is a task where the goal is to produce a viable example sentence that uses a target word with a target definition. The task is non-trivial for polysemous words, and previous works have only explored settings where ample labeled training data is available. In this paper, we demonstrate that exemplification modeling can be performed without a large labeled training corpus by either changing the format of the task (one-shot) or prompting large language models (few-shot), and ablate key components of our proposed one-shot and few-shot systems. We provide extensive automatic and human evaluations of model performance and find that our proposed one-shot and few-shot approaches perform similarly to a fully supervised baseline. We compare and contrast each method in terms of labeled training dataset size, performance, and model size, and find that each technique has at least one tradeoff that another approach does not.",
}
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<abstract>Exemplification modeling is a task where the goal is to produce a viable example sentence that uses a target word with a target definition. The task is non-trivial for polysemous words, and previous works have only explored settings where ample labeled training data is available. In this paper, we demonstrate that exemplification modeling can be performed without a large labeled training corpus by either changing the format of the task (one-shot) or prompting large language models (few-shot), and ablate key components of our proposed one-shot and few-shot systems. We provide extensive automatic and human evaluations of model performance and find that our proposed one-shot and few-shot approaches perform similarly to a fully supervised baseline. We compare and contrast each method in terms of labeled training dataset size, performance, and model size, and find that each technique has at least one tradeoff that another approach does not.</abstract>
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%0 Conference Proceedings
%T One-Shot and Few-Shot Exemplification Modeling
%A Harvill, John
%A Yoon, Hee Suk
%A Yoon, Eunseop
%A H a s e g a w a - J o h n s o n, Mark
%A Yoo, Chang
%Y Gehrmann, Sebastian
%Y Wang, Alex
%Y Sedoc, João
%Y Clark, Elizabeth
%Y Dhole, Kaustubh
%Y Chandu, Khyathi Raghavi
%Y Santus, Enrico
%Y Sedghamiz, Hooman
%S Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F harvill-etal-2023-one-shot
%X Exemplification modeling is a task where the goal is to produce a viable example sentence that uses a target word with a target definition. The task is non-trivial for polysemous words, and previous works have only explored settings where ample labeled training data is available. In this paper, we demonstrate that exemplification modeling can be performed without a large labeled training corpus by either changing the format of the task (one-shot) or prompting large language models (few-shot), and ablate key components of our proposed one-shot and few-shot systems. We provide extensive automatic and human evaluations of model performance and find that our proposed one-shot and few-shot approaches perform similarly to a fully supervised baseline. We compare and contrast each method in terms of labeled training dataset size, performance, and model size, and find that each technique has at least one tradeoff that another approach does not.
%U https://aclanthology.org/2023.gem-1.7
%P 76-87
Markdown (Informal)
[One-Shot and Few-Shot Exemplification Modeling](https://aclanthology.org/2023.gem-1.7) (Harvill et al., GEM-WS 2023)
ACL
- John Harvill, Hee Suk Yoon, Eunseop Yoon, Mark H a s e g a w a - J o h n s o n, and Chang Yoo. 2023. One-Shot and Few-Shot Exemplification Modeling. In Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), pages 76–87, Singapore. Association for Computational Linguistics.