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

Jianfei Wu, Xubin Wang, Weijia Jia

TL;DR

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.

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.

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

TL;DR

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.

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.
Paper Structure (14 sections, 4 equations, 1 figure, 2 tables)

This paper contains 14 sections, 4 equations, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Text Annotation via LLMs
  4. Prompts Enhancement in LLMs
  5. Methodology
  6. LLM Reasoning Process
  7. Rationale-Driven Collaborative Annotation Framework
  8. Output Restriction and Error Mitigation
  9. Similar Example Matching
  10. Experiments
  11. Datasets
  12. Experimental Setup
  13. Results and Analysis
  14. Conclusion

Figures (1)

  • Figure 1: This figure highlights the differences between universal self-consistency method and the rationale-driven collaborative annotation method. In universal self-consistency, multiple models generate outputs independently, which can result in inconsistencies. In contrast, the rationale-driven collaborative method allows LLMs to perform reasoning consecutively, with each round building on the previous output.