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 training paradigms.
翻译:本文介绍我们对第九次辩论采矿讲习班共同任务(2022年)的贡献,我们的方法是使用大语言模型来完成辩论质量预测任务,我们使用GPT-3进行快速工程,并调查培训模式的多任务学习、对比学习和中期任务培训。我们发现,一种混合的预测设置了单一模型。激励GPT-3在预测论证有效性方面最有效,而论证新颖性最好由利用所有三种培训模式培训的模型来估计。