Data scarcity is a common problem in NLP, especially when the annotation pertains to nuanced socio-linguistic concepts that require specialized knowledge. As a result, few-shot identification of these concepts is desirable. Few-shot in-context learning using pre-trained Large Language Models (LLMs) has been recently applied successfully in many NLP tasks. In this paper, we study few-shot identification of a psycho-linguistic concept, Morality Frames (Roy et al., 2021), using LLMs. Morality frames are a representation framework that provides a holistic view of the moral sentiment expressed in text, identifying the relevant moral foundation (Haidt and Graham, 2007) and at a finer level of granularity, the moral sentiment expressed towards the entities mentioned in the text. Previous studies relied on human annotation to identify morality frames in text which is expensive. In this paper, we propose prompting-based approaches using pretrained Large Language Models for identification of morality frames, relying only on few-shot exemplars. We compare our models' performance with few-shot RoBERTa and found promising results.
翻译:数据稀缺是国家语言方案的一个常见问题,特别是当说明涉及需要专门知识的细微社会语言概念时,数据稀缺是国家语言方案的一个常见问题,因此,最好能略微查明这些概念。最近,在很多国家语言方案的任务中成功地应用了经过事先训练的大型语言模型(LLMs)的少量文字学习。在本文件中,我们研究用LLMs(Roy等人,2021年)来说明精神语言概念、道德框架(Roy等人,2021年)的几分辨。道德框架是一个代表框架,它提供了对文字中表达的道德情绪的全面看法,确定了相关的道德基础(Haidt和Graham,2007年),在微小的颗粒度上,对案文中提到的实体表示的道德情绪。以前的研究依靠人类说明来确定昂贵的文字道德框架。在本文件中,我们建议采用经过事先训练的大型语言模型的快速方法来确定道德框架,只依靠几张照片的外表。我们比较了我们的模型与少数RoBERTa,发现有希望的结果。