JUAGE at SemEval-2023 Task 10: Parameter Efficient Classification
Jeffrey Sorensen, Katerina Korre, John Pavlopoulos, Katrin Tomanek, Nithum Thain, Lucas Dixon, Léo Laugier
The 17th International Workshop on Semantic Evaluation (SemEval-2023) Task 10: towards explainable detection of online sexism Paper
TLDR:
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-
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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.