语言模型如何理解礼貌?

Can Li, Bin Pang, Wenbo Wang, Lingshu Hu, Matthew Gordon, Detelina Marinova, Bitty Balducci, Yi Shang
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引用次数: 0

摘要

礼貌在社会交往中起着关键作用。先前的工作提出了一种基于svm的计算方法,用于使用包含维基百科和堆栈交换请求数据的语料库的语言特征来预测礼貌。为了扩展之前的工作,我们专注于使用相同的数据集评估最先进的语言模型在礼貌预测方面的性能。本研究采用了两个模型。首先,我们对BERT在礼貌数据上进行微调,然后使用微调模型进行礼貌预测。其次,我们使用ChatGPT来预测礼貌。结果表明,在Wikipedia和Stack Exchange数据上,经过微调的BERT和ChatGPT都获得了比最先进的结果更好的结果。微调后的BERT优于零射击ChatGPT,但ChatGPT可以为其预测提供解释。此外,经过微调的BERT在维基百科语料库上的表现比人类水平高出2.28%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How Well Can Language Models Understand Politeness?
Politeness plays a key role in social communications. Previous work proposed an SVM-based computational method for predicting politeness using linguistic features on a corpus that contains Wikipedia and Stack Exchange requests data. To extend this prior work, we focus on evaluating the performance of state-of-the-art language models on politeness prediction using the same dataset. Two models are applied in this study. First, we fine-tune BERT on politeness data and then use the fine-tuned model for politeness prediction. Second, we use ChatGPT to predict politeness. The results show that both fine-tuned BERT and ChatGPT achieved better results than the state-of-the-art results on both Wikipedia and Stack Exchange data. Fine-tuned BERT outperforms zero shot ChatGPT, but ChatGPT can provide explanations for its prediction. Moreover, fine-tuned BERT outperforms human-level performance by 2.28% on Wikipedia corpus.
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