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：数据稀缺是自然语言处理中的常见问题，尤其当标注涉及需要专业知识的细微社会语言学概念时。因此，对这些概念进行少样本识别具有重要价值。基于预训练大型语言模型（LLMs）的少样本上下文学习近期已成功应用于多项自然语言处理任务。本文研究利用LLMs对心理语言学概念"道德框架"（Roy等，2021）进行少样本识别。道德框架是一种表征框架，能全面呈现文本中的道德情感：既识别相关道德基础维度（Haidt和Graham，2007），又在更细粒度层面解析文本所提及实体蕴含的道德情感。既往研究依赖昂贵的人工标注来识别文本中的道德框架。本文提出基于预训练LLMs的提示策略，仅需少量示例即可完成道德框架识别。我们将模型性能与少样本RoBERTa进行对比，实验取得了令人鼓舞的结果。