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Will It Blend? Mixing Training Paradigms & Prompting for Argument Quality Prediction. (arXiv:2209.08966v2 [cs.CL] UPDATED)
Oct. 6, 2022, 1:16 a.m. | Michiel van der Meer, Myrthe Reuver, Urja Khurana, Lea Krause, Selene Báez Santamaría
cs.CL updates on arXiv.org arxiv.org
This paper describes our contributions to the Shared Task of the 9th Workshop
on Argument Mining (2022). Our approach uses Large Language Models for the task
of Argument Quality Prediction. We perform prompt engineering using GPT-3, and
also investigate the training paradigms multi-task learning, contrastive
learning, and intermediate-task training. We find that a mixed prediction setup
outperforms single models. Prompting GPT-3 works best for predicting argument
validity, and argument novelty is best estimated by a model trained using all
three …
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