Enhancing Text Annotation through Rationale-Driven Collaborative Few-Shot Prompting

TL;DR

This paper introduces a rationale-driven collaborative few-shot prompting approach that enhances the performance of large language models in text annotation tasks, reducing bias and improving efficiency on complex datasets.

Contribution

It presents a novel collaborative prompting framework that significantly improves LLM annotation accuracy compared to traditional methods.

Findings

Collaborative prompting outperforms baseline methods in complex tasks.

Six LLMs show consistent improvements with the proposed approach.

The framework reduces annotation bias and enhances efficiency.

Abstract

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.