@inproceedings{sorensen-etal-2023-juage,
title = "{JUAGE} at {S}em{E}val-2023 Task 10: Parameter Efficient Classification",
author = "Sorensen, Jeffrey and
Korre, Katerina and
Pavlopoulos, John and
Tomanek, Katrin and
Thain, Nithum and
Dixon, Lucas and
Laugier, L{\'e}o",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.166",
doi = "10.18653/v1/2023.semeval-1.166",
pages = "1195--1203",
abstract = "Using pre-trained language models to implement classifiers from small to modest amounts of training data is an area of active research. The ability of large language models to generalize from few-shot examples and to produce strong classifiers is extended using the engineering approach of parameter-efficient tuning. Using the Explainable Detection of Online Sexism (EDOS) training data and a small number of trainable weights to create a tuned prompt vector, a competitive model for this task was built, which was top-ranked in Subtask B.",
}
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<abstract>Using pre-trained language models to implement classifiers from small to modest amounts of training data is an area of active research. The ability of large language models to generalize from few-shot examples and to produce strong classifiers is extended using the engineering approach of parameter-efficient tuning. Using the Explainable Detection of Online Sexism (EDOS) training data and a small number of trainable weights to create a tuned prompt vector, a competitive model for this task was built, which was top-ranked in Subtask B.</abstract>
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%0 Conference Proceedings
%T JUAGE at SemEval-2023 Task 10: Parameter Efficient Classification
%A Sorensen, Jeffrey
%A Korre, Katerina
%A Pavlopoulos, John
%A Tomanek, Katrin
%A Thain, Nithum
%A Dixon, Lucas
%A Laugier, Léo
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sorensen-etal-2023-juage
%X Using pre-trained language models to implement classifiers from small to modest amounts of training data is an area of active research. The ability of large language models to generalize from few-shot examples and to produce strong classifiers is extended using the engineering approach of parameter-efficient tuning. Using the Explainable Detection of Online Sexism (EDOS) training data and a small number of trainable weights to create a tuned prompt vector, a competitive model for this task was built, which was top-ranked in Subtask B.
%R 10.18653/v1/2023.semeval-1.166
%U https://aclanthology.org/2023.semeval-1.166
%U https://doi.org/10.18653/v1/2023.semeval-1.166
%P 1195-1203
Markdown (Informal)
[JUAGE at SemEval-2023 Task 10: Parameter Efficient Classification](https://aclanthology.org/2023.semeval-1.166) (Sorensen et al., SemEval 2023)
ACL
- Jeffrey Sorensen, Katerina Korre, John Pavlopoulos, Katrin Tomanek, Nithum Thain, Lucas Dixon, and Léo Laugier. 2023. JUAGE at SemEval-2023 Task 10: Parameter Efficient Classification. In Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023), pages 1195–1203, Toronto, Canada. Association for Computational Linguistics.