The traditional data annotation process is often labor-intensive, time-consuming, and susceptible to human bias, which complicates the management of increasingly complex datasets. This study explores the potential of large language models (LLMs) as automated data annotators to improve efficiency and consistency in annotation tasks. By employing rationale-driven collaborative few-shot prompting techniques, we aim to improve the performance of LLMs in text annotation. We conduct a rigorous evaluation of six LLMs across four benchmark datasets, comparing seven distinct methodologies. Our results demonstrate that collaborative methods consistently outperform traditional few-shot techniques and other baseline approaches, particularly in complex annotation tasks. Our work provides valuable insights and a robust framework for leveraging collaborative learning methods to tackle challenging text annotation tasks.

传统的数据标注过程通常劳动密集、耗时且容易受到人为偏差的影响。本研究提出了一种基于理由的协作少样本提示新方法，以提高大型语言模型（LLMs）在文本标注中的效率和一致性。结果表明，该方法在复杂标注任务中优于传统少样本技术，为解决文本标注任务提供了有价值的见解和强有力的框架。

通过基于理由的协作少样本提示增强文本标注