JUAGE at SemEval-2023 Task 10: Parameter Efficient Classification
Jeffrey Sorensen, Katerina Korre, John Pavlopoulos, Katrin Tomanek, Nithum Thain, Lucas Dixon, Léo Laugier
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.- Anthology ID:
- 2023.semeval-1.166
- Volume:
- Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Giovanni Da San Martino, Harish Tayyar Madabushi, Ritesh Kumar, Elisa Sartori
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1195–1203
- Language:
- URL:
- https://aclanthology.org/2023.semeval-1.166
- DOI:
- 10.18653/v1/2023.semeval-1.166
- Cite (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.
- Cite (Informal):
- JUAGE at SemEval-2023 Task 10: Parameter Efficient Classification (Sorensen et al., SemEval 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.semeval-1.166.pdf