One of the most powerful and enduring ideas in written discourse analysis is that genres can be described in terms of the moves which structure a writer’s purpose. Considerable research has sought to identify these distinct communicative acts, but analyses have been beset by problems of subjectivity, reliability, and the time-consuming need for multiple coders to confirm analyses. In this article, we employ the affordances of Generative Pre-trained Transformer 4 (GPT-4) to automate the annotation process by using natural language prompts. Focusing on abstracts from articles in four applied linguistics journals, we devise prompts which enable the model to identify moves effectively. The annotated outputs of these prompts were evaluated by two assessors with a third addressing disagreements. The results show that an eight-shot prompt was more effective than one using two, confirming that the inclusion of examples illustrating areas of variability can enhance GPT-4’s ability to recognize multiple moves in a single sentence and reduce bias related to textual position. We suggest that GPT-4 offers considerable potential in automating this annotation process, when human actors with domain-specific linguistic expertise inform the prompting process.

中文翻译：

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GPT-4 可以学习分析研究文章摘要中的动作吗？


书面话语分析中最强大和持久的思想之一是，体裁可以用构建作家目的的动作来描述。大量研究试图识别这些不同的交际行为，但分析受到主观性、可靠性以及需要多个编码人员来确认分析的耗时问题的困扰。在本文中，我们采用 Generative Pre-trained Transformer 4 （GPT-4） 的功能，通过使用自然语言提示来自动化注释过程。我们专注于四本应用语言学期刊文章的摘要，设计了使模型能够有效识别动作的提示。这些提示的注释输出由两名评估员评估，第三名评估员解决分歧。结果表明，八次提示比使用 2 次提示更有效，这证实了包含说明可变区域的示例可以增强 GPT-4 在单个句子中识别多个动作的能力，并减少与文本位置相关的偏差。我们认为 GPT-4 在自动化这个注释过程方面提供了相当大的潜力，当具有特定领域语言专业知识的人类行为者为提示过程提供信息时。