Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting
It addresses inefficiencies in data annotation for researchers and practitioners, but is incremental as it builds on existing few-shot prompting methods.
This study tackled the problem of labor-intensive and biased text annotation by using large language models with rationale-driven collaborative few-shot prompting, showing that collaborative methods consistently outperformed traditional few-shot techniques and baselines across six LLMs and four benchmark datasets.
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.